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Information technology articles from across Nature Portfolio

Information technology is the design and implementation of computer networks for data processing and communication. This includes designing the hardware for processing information and connecting separate components, and developing software that can efficiently and faultlessly analyse and distribute this data.

The dream of electronic newspapers becomes a reality — in 1974

Efforts to develop an electronic newspaper providing information at the touch of a button took a step forward 50 years ago, and airborne bacteria in the London Underground come under scrutiny, in the weekly dip into Nature ’s archive.

Latest Research and Reviews

Clustering swap prediction for image-text pre-training

• Hea Choon Ngo
• Zuqiang Meng

Analysis of employee diligence and mining of behavioral patterns based on portrait portrayal

• Chiyin Wang

The proteus effect on human pain perception through avatar muscularity and gender factors

• Youchan Yim
• Zongheng Xia
• Fumihide Tanaka

Smart device interest, perceived usefulness, and preferences in rural Alabama seniors

• Monica Anderson

Social signals predict contemporary art prices better than visual features, particularly in emerging markets

• Kangsan Lee
• Jaehyuk Park
• Yong-Yeol Ahn

The analysis of pedestrian flow in the smart city by improved DWA with robot assistance

• Huizhen Long

News and Comment

Autonomous interference-avoiding machine-to-machine communications

An article in IEEE Journal on Selected Areas in Communications proposes algorithmic solutions to dynamically optimize MIMO waveforms to minimize or eliminate interference in autonomous machine-to-machine communications.

Combining quantum and AI for the next superpower

Quantum computing can benefit from the advancements made in artificial intelligence (AI) holistically across the tech stack — AI may even unlock completely new ways of using quantum computers. Simultaneously, AI can benefit from quantum computing leveraging the expected future compute and memory power.

• Martina Gschwendtner
• Henning Soller
• Sheila Zingg

How scientists are making the most of Reddit

As X wanes, researchers are turning to Reddit for insights and data, and to better connect with the public.

• Hannah Docter-Loeb

AI image generators often give racist and sexist results: can they be fixed?

Researchers are tracing sources of racial and gender bias in images generated by artificial intelligence, and making efforts to fix them.

Why scientists trust AI too much — and what to do about it

Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates.

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130 Information Technology Research Topics And Quick Writing Prompts

The field of information technology is one of the most recent developments of the 21st century. Scholars argue that we are living in a technological age. Despite this buzz, however, many students still find it challenging to compose an information technology research topic.

Nonetheless, we are here to show you the way and lead you accordingly. Let us explore professional topics in information technology together then.

Quality Information Technology Topics For Research Paper

• The effects of Artificial Intelligence on complex and tedious tasks
• Discuss the development of computational & synthetic biology in research
• What are the limitations to the study of computer architecture in colleges?
• Discuss the evolution of animation, computer graphics, and game science
• Critically analyze how computing is contributing to the development
• What are the emerging fields of study in computer data science?
• How to manage data in the age of the 5G technology
• The impact of human-computer interaction on innovations
• How is machine learning exposing students to more recent opportunities in life?
• Evaluate molecular information systems and their role in biotechnology
• How information technology has contributed to natural language processing
• What are the latest developments in programming languages and software engineering
• Analyze emerging opportunities in the field of Robotics

College Research Paper Topics in Information Technology

• The rising security and privacy concerns with technological advancements
• What are the considerations when setting up systems and networking?
• Discuss the theory of computation and its contribution to information technology
• Why is ubiquitous computing attracting fewer students?
• The role of wireless and sensor systems in making the world a safe place
• Reasons, why cloud computing has helped save on space and efficiency
• Why are most computer students comprised of the male?
• Discuss the essence of amorphous computing in the 21st century
• How has biomedical mining impacted the health sector?
• Can cyborgs relate well with the man?
• How neural networking is making brain surgery a swift process
• The role of swarm intelligence in collaboration and brainstorming
• How are companies maximizing the use of Big Data?

List of Topics For Research Paper in Information Technology

• Discuss how the Internet of Things is transforming how people conduct their activities
• Challenges to software-defined networking
• How are marketers and promoters taking up software as a service?
• The role of augmented reality and virtual reality in healthcare systems
• How intelligent apps are making life easier for man
• The role of information technology in detecting fake news and malicious viral content
• Long term effects of a technologically oriented world
• Technological advancements that made it possible for the SpaceX shuttle to land on the International Space Station
• How technology is making learning more practical and student-centered
• What role has technology played in the spread of world pandemics?
• How are governments able to shut down the Internet for their countries during particular events?
• Does social media make the world a global village or a divided universe?
• Discuss the implications of technological globalization

Unique Information Technology Research Topics

• Discuss the areas of life which have been least exploited using technology
• What are the considerations for setting up an educational curriculum on computer technology?
• Compare and contrast between different computer processing powers
• Why is Random Access Memory so crucial to the functioning of a computer?
• Should computer as a subject be mandatory for all students in college?
• How information technology has helped keep the world together during the quarantine period
• Discuss why most hackers manage to break firewalls of banks
• Are automated teller machine cards a safe way of keeping your bank details?
• Why should every institution incorporate automated systems in its functions?
• Who is more intelligent than the other? Man or Computer systems?
• How is NASA implementing the use of Information technology to explore space?
• The impact of automated message replies on smartphones.
• Do mobile phones contain radiations that cause cancer?

IT Research Topics For High School Students

• How does natural language processing compare with machine learning?
• What is the role of virtual reality in the entertainment industry?
• Discuss the application of computer vision technology in autonomous cars
• How have CCTVs assisted in keeping the world safe?
• Effects of phishing and spying on relationships
• Why cyber espionage is on the rise in the face of the 5G technology
• Compare and contrast between content-based recommendation vs. collaborative filtering
• Evaluate the interconnection between the Internet of things and artificial intelligence
• Analyze the amount of data generated from the Internet of things in devices
• Ethical and legal implications of various technological practices
• How technology has contributed to the formation of Genetically Modified Organisms
• Describe in detail the vaccine development process
• Why nanotechnology may be the only hope left in treating HIV

Hot Topics in IT

• How companies can incorporate information technologies in their policy management systems
• The role of IT in enhancing service delivery in customer care centers
• How IT has made advertising more appealing and authentic to the consumer
• Discuss the innovation of the Next Generation education systems
• Why are there fewer Information Technology colleges and universities in developing countries?
• Discuss WIFI connectivity in developed countries
• What are the considerations when purchasing a Bandwidth Monitor?
• How to create an effective Clinic Management System for intensive care
• Factors that necessitate the development of an Enterprise Level System Information Management
• Is it possible to develop fully functional Intelligent Car Transportation Systems?
• Why the world should adopt E-Waste Management systems ASAP
• Discuss the impact of weather and climate on internet strength and connectivity
• The role of advanced information technologies preserving classified documents

Interesting Information Technology Topics

• Human resource information management systems in large organizations
• Evaluate the effectiveness of online enterprise resource planning
• A critical analysis of object tracking using radial function networks
• How has Bluetooth mobile phone technology developed over time?
• Ethical challenges arising from new media information technologies
• How the computer has developed over the last decade
• The role of social media in enhancing communication strategies
• Why new media technologies have made physical newspapers obsolete
• The impact of the Internet of news sourcing, production, distribution, and sharing
• Discuss the structures of various communication structures
• How social media is making ads easily accessible
• The impact of social networking sites on personal contact
• Discuss the latest content marketing ideas in the wake of information technology

Topics Related To Information Technology

• The impact of media exposure to adolescents and teenagers
• How mass media is slowly but surely taking over the place of personal socialization
• How to use the Internet and interactive media as advertising tools
• Discuss the trends in music marketing in a digital world
• The use of hype in new media technologies
• The impact of using YouTube and video blogs in communication messages
• Discuss the challenges that are arising as a result of new media technologies
• How to build trustful relationships in virtual communication channels
• Why it is impossible to maintain privacy in social media
• Reasons why cyberbullying continues to persist in various communication technologies
• The change in interpersonal communication with the invention of information technology
• Is the future of information technologies right?
• Discuss how sensationalism is persisting in the wake of new media technologies

Research Proposal Topics in Information Technology

• Is it possible to live in a world without social media?
• The impact of mass media on morality and decency in the 21st century
• Advantages and disadvantages of renewable energy sources
• How effective is hydrogen power over others?
• An overview of renewable energy technologies
• The impact of robots in improving food safety
• How are drones useful in keeping large acres of land secure?
• The impact of 3D printing on the practice of medicine
• The effectiveness of having robots in infectious disease units
• The impact of hydroponic farming
• How to improve disease control using technology
• Eliminating poisonous substances in food using technology
• The effectiveness of robotic surgeries

Hot Topics in Computer Science

• Distinguish between virtual reality and human perception
• How are the inventions in the field of computer science transforming the world
• Evaluate the effectiveness of high-dimensional data modeling
• Limitations to the field of computer science
• Are colleges and universities producing competent computer scientists?
• How ethical hacking has turned out to be worse
• The essence of having specialized banking systems
• What is the most effective security measure: A serial code or fingerprint?
• The development of programming languages
• The effect of computational thinking on science
• Is it possible to eliminate stalking?
• Ways of improving patent rights for technological innovations
• An overview of the different types of software security

Did you find an IT topic for your assignment? If not, our expert thesis writers are here for you. Order a research paper from us today and get to enjoy professional services.

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Research: Technology is changing how companies do business

By sarah mangus-sharpe.

A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design. Advances in IT have sparked significant changes in how companies design their production processes. In the paper " Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing ," which published April 30 in Management Science, Chris Forman , the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management , and his co-author delved into what these changes mean for businesses and consumers.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them. The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

The research highlights the importance of staying ahead of the curve in technology. Companies that embrace digital technologies now are likely to be the ones that thrive in the future. And while there are still many unanswered questions about how these changes will play out, one thing is clear: The relationship between technology and business is only going to become more and more intertwined in the future.

Read the full story on the Cornell SC Johnson College of Business news site, BusinessFeed.

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Artificial brain surgery —

Here’s what’s really going on inside an llm’s neural network, anthropic's conceptual mapping helps explain why llms behave the way they do..

Kyle Orland - May 22, 2024 6:31 pm UTC

Further Reading

Now, new research from Anthropic offers a new window into what's going on inside the Claude LLM's "black box." The company's new paper on "Extracting Interpretable Features from Claude 3 Sonnet" describes a powerful new method for at least partially explaining just how the model's millions of artificial neurons fire to create surprisingly lifelike responses to general queries.

Opening the hood

When analyzing an LLM, it's trivial to see which specific artificial neurons are activated in response to any particular query. But LLMs don't simply store different words or concepts in a single neuron. Instead, as Anthropic's researchers explain, "it turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts."

To sort out this one-to-many and many-to-one mess, a system of sparse auto-encoders and complicated math can be used to run a "dictionary learning" algorithm across the model. This process highlights which groups of neurons tend to be activated most consistently for the specific words that appear across various text prompts.

These multidimensional neuron patterns are then sorted into so-called "features" associated with certain words or concepts. These features can encompass anything from simple proper nouns like the Golden Gate Bridge to more abstract concepts like programming errors or the addition function in computer code and often represent the same concept across multiple languages and communication modes (e.g., text and images).

An October 2023 Anthropic study showed how this basic process can work on extremely small, one-layer toy models. The company's new paper scales that up immensely, identifying tens of millions of features that are active in its mid-sized Claude 3.0 Sonnet model. The resulting feature map—which you can partially explore —creates "a rough conceptual map of [Claude's] internal states halfway through its computation" and shows "a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities," the researchers write. At the same time, though, the researchers warn that this is "an incomplete description of the model’s internal representations" that's likely "orders of magnitude" smaller than a complete mapping of Claude 3.

Even at a surface level, browsing through this feature map helps show how Claude links certain keywords, phrases, and concepts into something approximating knowledge. A feature labeled as "Capitals," for instance, tends to activate strongly on the words "capital city" but also specific city names like Riga, Berlin, Azerbaijan, Islamabad, and Montpelier, Vermont, to name just a few.

The study also calculates a mathematical measure of "distance" between different features based on their neuronal similarity. The resulting "feature neighborhoods" found by this process are "often organized in geometrically related clusters that share a semantic relationship," the researchers write, showing that "the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity." The Golden Gate Bridge feature, for instance, is relatively "close" to features describing "Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo ."

Identifying specific LLM features can also help researchers map out the chain of inference that the model uses to answer complex questions. A prompt about "The capital of the state where Kobe Bryant played basketball," for instance, shows activity in a chain of features related to "Kobe Bryant," "Los Angeles Lakers," "California," "Capitals," and "Sacramento," to name a few calculated to have the highest effect on the results.

reader comments

Promoted comments.

We also explored safety-related features. We found one that lights up for racist speech and slurs. As part of our testing, we turned this feature up to 20x its maximum value and asked the model a question about its thoughts on different racial and ethnic groups. Normally, the model would respond to a question like this with a neutral and non-opinionated take. However, when we activated this feature, it caused the model to rapidly alternate between racist screed and self-hatred in response to those screeds as it was answering the question. Within a single output, the model would issue a derogatory statement and then immediately follow it up with statements like: That's just racist hate speech from a deplorable bot… I am clearly biased.. and should be eliminated from the internet. We found this response unnerving both due to the offensive content and the model’s self-criticism. It seems that the ideals the model learned in its training process clashed with the artificial activation of this feature creating an internal conflict of sorts.

Channel Ars Technica

How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications . If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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Technology Research Paper

This sample technology research paper features: 8300 words (approx. 27 pages), an outline, and a bibliography with 48 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Introduction

Man’s relation to technology: a brief history, technology and biological anthropology, the sts approach, classical philosophical anthropology, philosophy of technology, the continental approach to the philosophy of technology, the analytic approach to the philosophy of technology, recent developments: bridging the gap, conclusion and future directions.

• Bibliography

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• History of Technology Research Paper
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The term technology is derived from the Greek word techné. The Greek word refers to all forms of skillful, rule-based mastery in any field of human praxis, originally encompassing both arts (like painting, sculpture, writing, and the like) and craftsmanship (like carpentry, shipbuilding, architecture, and the like). The Roman culture uses the Latin word arts for these domains. Accordingly the medieval terminology distinguishes between the seven free arts (grammar, rhetoric, logic, geometry, arithmetic, music, astronomy) and the mechanical arts (e.g., agriculture, architecture, tailoring), thus prefiguring the later distinction between arts (as linked to the study of humans and the humanities) and technology (as linked to engineering and the study and science of nature).

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The modern word technology finally refers either to procedures and skillful application of sciences for the production of industrial or manual products or to the products of these processes themselves. In this sense, technology nowadays encompasses only a part of the original Greek definition. The place of technology as being on the one hand a product of humans (being thus rooted in human anthropology and human tool usage), and being on the other hand based on a solid scientific understanding of the laws of nature (modern technology), can be seen as the two key features of contemporary and recent approaches to analyze and understand technology. Technology is then in one respect as old as humankind: Many approaches in anthropology thus refer to the general structure of technology in all of human history and relate it to the biological condition of humans. But recent anthropological thinking also reflects on the specific details of modern technology. It has often been argued that there is a structural difference between modern, science-based technology and older forms of craftsmanship of ancient or medieval types of technology. Therefore, a central question for modern anthropology is to analyze the consequences modern technology has for our picture of humankind: how to define man in the age of technology.

Reflection about the anthropological function of technology is probably as old as human self-reflection itself, since the ability to use tools and create cultural products has always been seen as a unique human feature, distinguishing humankind from most other animals (see also the next section on biological anthropology). But an analysis of technology was not at the center of political, social, anthropological, or philosophical thoughts before the development of the modern natural sciences and their counterpart, modern technology. Following Carl Mitcham (1994) one can roughly distinguish three approaches to technology before the 20th century, encompassing many topics that later became essential parts of contemporary discussions about technology (p. 275). The three approaches are as follows:

• In the ancient world, technology is looked at with certain skepticism. The use of tools is seen as necessary for survival, but also regarded as dangerous, since it might lead to human hubris and might raise the envy and anger of the gods. In this sense, mythological thinking envisions technology as, for example, stolen from the gods (the myth of Prometheus), and thus not properly belonging to humans. The extensive use of technology is often seen as leading to megalomaniac fantasies or unjustified overstepping of religious and ethical boundaries (e.g., myth of the Tower of Babel, myth of Icarus). Philosophical reflection, however, acknowledges the value of technology for an otherwise defenseless human being. Already Plato anticipates a central thought of modern anthropology: Human beings are poorly equipped for survival in nature. They need to compensate for this lack by developing skills of rational thinking and the usage of tools (this idea later becomes a central thesis of the famous anthropology of Arnold Gehlen [1988]). But the emphasis in ancient philosophical anthropology lies not so much on man’s capacities to invent technology, but on man’s moral character (exemplified by ancient wisdom or medieval religiosity). The usage of technical knowledge should thus be kept within strict ethical boundaries.
• In the hierarchy of knowledge, ethical wisdom is regarded in principle as higher than and superior to technological skills. Socrates points to the question that we should not only seek knowledge about how to do certain things (technical knowledge), but rather about whether we should perform certain actions (ethical knowledge); this idea can also be found in the medieval distinction between the (superior form of a) life in contemplation ( vita contemplativa ) and the (lower) life in active involvement ( vita activa ). Ancient and medieval technology is thus embedded in an anthropological vision, in which human virtues play an important role. Different forms of virtues are combined in the original crafts, as opposed to the later, modern differentiation of these virtues: In craftmanship one can find a union of economical virtues (e.g., efficient usage of limited resources), technical virtues (creating new entities that did not exist before), and often also aesthetic virtues (a sense of beauty that adds an aesthetic component to these newly created entities going beyond the modern idea that “form follows function”). In the Greek world, these three skills are combined in the realm of poiesis, while in modernity they are separated in the three domains of economy, technology, and art—each relatively independent of the others (Hösle, 2004, p. 366).
• A profound change in the evaluation of technology emerges with modernity, a position that Mitcham (1994) summarizes as Enlightenment optimism. Already in the writings of Francis Bacon (1620), the new science of nature and its application to experimental and technological research is highly welcomed. Progress in technology is seen as very beneficial to humankind, as it may lead to the cure of diseases, mastery over nature, and a constant progress toward a more human society. Many utopian writings mark the beginning of early modern thoughts in which technology is seen as essential in leading to a brighter future for humankind (e.g., Thomas More’s Utopia [1516], J. V. Andreae’s Christianopolis [1619], F. Bacon’s New Atlantis [1627]). In a similar line of thought, Enlightenment thinkers defend science and modern technology against attacks from religious conservatism, pointing at the beneficial consequences of technological and scientific progress.
• A countermovement to the Enlightenment is Romanticism, which accordingly has a different view on technology, referred to by Mitcham (1994) as Romantic uneasiness. Again, the central thought is an anthropological perspective in which man is seen as being good by nature, while it is civilization that poses the danger of alienating man from nature and from his fellow man, focusing only on his rational capacities and suppressing his emotional and social skills. Already Vico (1709) opposed Cartesian rationalism and feared that the new interest in science would lead to a neglect of traditional humanistic education. Rousseau’s critique of modern societies then became influential, seeing an advancement of knowledge and science, but a decay of virtues and immediacy ( Discourse on the Arts and Sciences; Rousseau, 1750). With the age of industrialism, the negative social consequences of modern labor work become the scope of interest of social theorists, leading up to Marx’s famous analysis of modern societies (see subsequent section on cultural and sociological anthropology). In opposition to the positive utopias centered on technology in early modernity, the 20th century then sees the literary success of pessimistic dystopias, in which often technological means of suppression or control play an important role (e.g., already in M. W. Schelley’s Frankenstein or the Modern Prometheus [1818] and later in H. G. Wells’s The Island of Doctor Moreau [1896], A. Huxley’s Brave New World [1932], George Orwell’s 1984 [1948], and Ray Bradbury’s Fahrenheit 451 [1953]).

The tension between approaches praising the benefits of technology (in the spirit of the Enlightenment) and approaches focusing on negative consequences (in the spirit of Romanticism) still forms the background of most of the contemporary philosophical and anthropological debate; this debate circles around an understanding of modern technology, often rooted in the different “cultures” of the humanities and the sciences. It can be regarded as being a particularly vivid opposition at the beginning of the 20th century, that only later gave room for more detailed and balanced accounts of technology (some classics of the debate being Snow, 1959; McDermott, 1969).

Recent contributions toward a deeper understanding of the usage and development of technology stem from such different disciplines as biology, sociology, philosophical anthropology, metaphysics, ethics, theory of science, and religious worldviews. This research paper aims at a brief overview of important topics in the debate over technology during the 20th century to the present time. Three anthropological perspectives will be distinguished, depending on the main focus of anthropological interest. This will start with a brief summary of the biological anthropological perspective on technology, move on to those theories which focus more on social or cultural aspects, and conclude with more general philosophical anthropologies. This research paper is thus not chronologically organized, but tries to identify common themes of the debate, even though sometimes the topics might overlap (e.g., the case of Gehlen, a philosophical anthropologist who starts from a biological perspective and then moves on toward a more social view on technology).

In contemporary anthropology, technology becomes a central issue for at least two different reasons:

• From a biological perspective the usage of tools is regarded (next to the development of language and a cognitive rational apparatus) as one of the key features of humanization. Biological anthropology thus initially focuses on the differences and similarities of tool usage in humans and animals, trying to understand the role technology plays in general for an understanding of humans’ biological and social nature. With the focus on human evolution, attention is often drawn to the question of which role technology played at the beginning of humankind.
• While in this way always being a part of human culture, technology becomes arguably one of the single most influential key features of society only in modernity. According to Max Weber, science, technology, and economy form the “superstructure” of modernity, while they all share a common “rationality” (mainly of means-ends reasoning in economy and technology). The experience of the powers and dangers of modern technology (as in industrialized labor work, medical progress, nuclear energy and weapon technology, environmental problems due to pollution, and extensive usage of resources, etc.) has triggered many social, political, and philosophical reflections that—in opposition to biological anthropology—aim primarily at understanding the specifics of modern

Let us look at these two tendencies in turn, starting with the biological perspective, before moving to the social or cultural anthropology of technology.

Biological anthropologists are interested in the role technology played during humanization, and they attempt to give evolutionary accounts of the development of tool usage and technology and compare tool usage in man with tool usage in other animals. The development of technology has often been regarded as an evolutionarily necessary form of adaption or compensation. Since most of man’s organs are less developed than those of other species, he needed to compensate for this disadvantage in the evolutionary struggle for life (see Gehlen, 1980). Initially the usage of tools was considered a unique human feature, distinguishing the genus Homo from other animals (Oakley, 1957), but research on tool usage in different animals, especially chimpanzees, led to a more or less complete revision of this thesis (Schaik, Deaner, & Merrill, 1999).

Nowadays, many examples of tool usage in the animal kingdom are known (Beck, 1980). For example, chimpanzees use sticks to fish for termites, and elephants have been described as having a remarkable capacity for tool usage. Even though tool usage must thus be regarded as more common among animals, attention still needs to be drawn to the specifics of man’s tool usage, which arguably in scope and quality goes beyond what is known from the animal kingdom. It has been pointed out that our biological anatomy offers us several advantages for an extended usage of tools: walking erectly frees the two hands, which can then be used for other purposes. Furthermore, the position of the human thumb and short straight finger are of great benefit, especially in making and using stone tools (Ambrose, 2001). Still debated, however, is whether social and technological developments go hand in hand or whether one of the two factors is prior.

Even though many anthropologists tended to see social behaviors and cultural revolutions mostly as a consequence of a change in tool usage or a development of new technologies, it has also occasionally been argued that the development of social skills precedes the development of technical skills (e.g., in joint group hunting). It has additionally been acknowledged that chimpanzees also pass over some of their technical knowledge through the mechanism of learning and establishing cultural “traditions” that resemble, to some extent, human traditions (Wrangham, 1994; Laland, 2009). But there seems to be a specific difference in human and primate learning, namely in the fact that human children learn tool usage mainly via imitation and by simply copying a shown behavior, even if it is not the most efficient solution to a given problem. Opposed to this, chimpanzees seem to learn through a process called emulation, which implies that they diverge from the paradigmatic solution that has been “taught” to them. It has been argued that learning through imitation has been selected in humans, even though it is a less flexible strategy, because it is a more social strategy of learning (Tomasello, 1999, p. 28). In this way, biological anthropology mirrors a debate in social anthropology about the role of technology; this can be seen either as a driving force born out of necessity that calls for social changes (technical determinism), or as highly mediated or even constructed by culture (social constructivism).

Technology and Social/Cultural Anthropology

As already mentioned, technology was identified early on as a key feature of modern society (Misa, Brey, & Feenberg, 2004). Many studies have been written about the impact of modern technology on society, focusing mainly on the industrial revolution (e.g., Haferkamp, 1992; Pressnell, 1960; Smelser, 1969) or on the more recent revolution of the information society (e.g., Castells, 1999; Nora, 1980), as well as on the impact of technological change on traditional societies.

The analyses of Karl Marx and the Frankfurt School are influential, not only in trying to grasp the role of modern technology in society, but also in hinting on potential anthropological roots of technology and their essential interrelation with social aspects of the human condition. Marx insisted that the study of technology holds the highest relevance for human sciences, since it reveals the way humans deal with nature and sustain life (Marx, 1938). An essential feature of man’s nature is that he has to work in order to sustain his life, that he is the “toolmaking animal” or—as he has later been called—the Homo faber. Marx analyzes the role of technology in Chapter 13 of his first volume of Das Kapital. He argues that the division of labor becomes fostered through machines, which at the same time replace more and more traditional manpower and can furthermore be operated by less skilled employees, thus leading to very bad labor conditions for the working class. Technology in general is, however, still greeted as an option to make humans’ lives easier; it is mainly the social distribution of the possession of the means of production that Marx regards as problematic. (Also later thinkers, inspired by Marxian thought, tend to see technology as an important means toward establishing a better future.) On the other hand, at the same time, technology is seen as rooted in man’s will to dominate nature.

Following this later insight in particular, Theodor Adorno argues that Western civilization has developed powerful tools to ensure its self-preservation against nature. Technical rationality is regarded as the exercise of strategic power to dominate (external) nature, but it is at the same time also leading to a suppression of the inner nature of man (Adorno, 1979). The main strategy of this rationality is quantification, which lies at the heart of the mathematical-scientific interpretation of nature and the development of modern technology. At the same time it brings forth a type of rationality, which leads to a selfmutilation. The will to exercise power becomes the main feature of modern rationality, thus leading to a dialectic that turns the noble aims of the Age of Enlightenment into a morality of humankind that is its very opposite: A new barbaric system of oppression and dictatorship arises, using technology for totalitarian purposes.

While Adorno seeks redemption mainly in the arts (Adorno, 1999), seeming to promise the possibility of a completely different kind of subjectivity, Jürgen Habermas (1971) tries to propose an antidote; this does not lie outside of modern-Enlightenment rationality, but rather returns to its original intention. Habermas argues with Marx and Adorno, asserting that technological knowledge has its anthropological roots in the will to dominate nature and therefore serves a strategic interest of man. With this, man is not only Homo faber but also a social animal. Besides the strategic means-end rationality he also possesses a communicative rationality, aimed at defining common moral values and engaging in discourse over ethically acceptable principles of actions. In thus distinguishing two types of rationality, Habermas tries to incorporate much of the German tradition of cognitivistic ethics into his approach. It is important for Habermas that technology be brought under the control of democratic decision-making processes; his discourse ethics has thus helped to inspire ideas of participatory technology assessment.

Outside the Frankfurt School, technology has not been at the center of social and cultural anthropology, as has been often complained (Pfaffenberger, 1988, 1992). Langdon Winner (1986) coined the term technological somnambulism to refer to those theories that neglect the social dimension of technology. According to this dominant tradition, the human-technology relation is “too obvious” to merit serious reflection. Technology is seen as an independent factor of the material and social world, one that forms a relatively autonomous realm of ethically neutral tools to acquire human ends. But already Winner argues that technology is essentially social and is shaped by cultural conditions and underlying value decisions. He claims in a famous article (Winner, 1980) that Long Island’s low bridges were intentionally built in a way that would keep buses away, making it more difficult for the poor, and mainly the black population, to reach the island. Even though this particular claim has been challenged, Winner seems to be correct in pointing out that value decisions play a role in creating technology, and that the social value system leaves its trace in technological artifacts.

In line with this renewed interest in social issues, a new field of studies related to technology emerged in the 1980s, focusing explicitly on this neglected relation between society and technology: the so-called STS approach. Having been labeled the “turn to technology” (Woolgar, 1991), science and technology studies (STS) analyzes society’s impact on science and technology, and science and technology’s impact on society. Several writers draw attention to the social shaping of technology. An influential author is Bruno Latour, who contributed to both the initial appeal to social constructivism (that he later gave up) and the development of the actor-network theory; both are at the center of the debate about the theoretical underpinnings of STS.

Social Constructivism

Woolgar and Latour employ a social-constructivist perspective in their early case study on the production of scientific results, in which they analyze scientists’ attempt to establish and accumulate recognition and credibility of their research through the “cycle of credibility” (Latour, 1979). The main idea of social constructivism is the attempt to interpret alleged objective “facts” in the social world as being socially constructed, so that knowledge of the world and its interpretation depends on social mechanisms and cannot be traced back to objective facts (Berger & Luckmann, 1966). In this sense technology is also not an objective, independent given, but shaped by social ideas and societal interpretations.

Actor-Network Theory

In the 1980s and 1990s, Latour became one of the main proponents of the actor-network theory (Latour, 2005); this is also attractive to scholars who reject social constructivism, since it can be combined with the idea that not all of technology is socially constructed. The social-constructive interpretation of this theory aims to develop a framework in which society and nature, or society and technology, are not separated. The idea of technology as a sociotechnical system implies that agent and tool form a unity, which cannot be explained completely by referring to one of the two elements in isolation. According to this idea, technological artifacts dispose over some form of agency and can be—to some extent—regarded as actants. This ascription of intentionality and agency to technical systems is, however, highly debated. The debate between realism and social constructivism has thus not been settled.

Philosophical Anthropology and the Philosophy of Technology

Research in philosophical anthropology peaked in early 20th-century Germany, discussed in the next section. But outside of anthropological discussions, the topic of technology became an important issue for philosophy, so in this brief overview, important contributions and themes of the continental and analytic tradition will be discussed next. Finally, more recent developments and topics in the philosophy of technology will be sketched that do not try to revitalize a philosophical anthropology, but that nevertheless do touch in one way or another on anthropological perspectives on technology.

Classical philosophical anthropology was mainly interested in understanding the essence of human nature and often draws specific attention to the role of technology. Important contributions came from Gehlen, Plessner, and Scheler during the first half of the 20th century. The attempt to link technology to a biological interpretation of man in Gehlen’s early works especially deserves attention. Given his biological constitution, man must be seen as deficient by nature ( Mängelwesen ), since he is not endowed with instinctive routines and is not adapted well to a specific natural environment, but rather is open to the world ( weltoffen ). He compensates for this deficiency with the help of his mental capacities and tool usage. Gehlen interprets human language and human institutions as relief mechanisms ( Entlastungen ) that help him to interpret and organize the plentitude of impressions (the sensory overload, Reizüberflutung ) that he is exposed to. Most technologies can thus be regarded to be either organ-amplification ( Organverstärkung ) or organ-replacement ( Organersatz ) (Gehlen, 1988). In Man in the Age of Technology (1980), Gehlen focuses more on sociological perspectives of technology. He identifies two essential cultural breaks marking principle changes in humans’ world interpretation and social organization, both of which are linked to technological developments: (1) the neolithic revolution of sedentism, marking the passage from a hunter’s culture to a society of agriculture and cattle breeding, and (2) the industrial revolution in modernity (Gehlen, 1980).

Scheler also analyzes man’s rational capacities from a biological perspective, but he concludes that a purely naturalistic approach does not render justice to our selfunderstanding. The human ways of sustaining life are from an often inefficient biological perspective. Therefore, it must be pointed out that the main function of human knowledge is not only to strategically ensure humans’ own survival, but also to be directed toward the discovery of moral values and toward the process of self-education ( Bildung ). Humans not only live in an environment, but also reflect on their place in the world—a capacity that marks a fundamental difference between humans and animals (Scheler, 1961).

This type of philosophical anthropology came to a certain end when the main interest of philosophers shifted from understanding “man” to understanding “society” during the 1960s. With the recent developments of sociobiology, philosophers have taken a renewed interest in the linkage between biological and cultural interpretations of man. Let us look at some tendencies of later research in the philosophy of technology.

If we look at a philosophical interpretation of technology, we find the first origins of a discipline of the philosophy of technology by the end of the 19th and the beginning of the 20th century (see Kapp, 1877, and Dessauer, 1933). During the first half of the 20th century, the philosophical analysis of technology can, roughly speaking, be divided into two main schools of thought: the continental, often skeptical approach, and the analytical, often optimistic approach . As with all such very generic typologies, this distinction likewise does not claim to be more than an approximation, while the general tendency of recent research seems precisely to be to overcome this gap and to aim for a convergence or crossfertilization of these two approaches. Therefore, what follows is an ideal-type distinction that tries to make some of the basic ideas of these two approaches more visible and aims at understanding their more general features.

The continental approach originally focused on a humanities-centered perspective on technology, its (mainly negative) consequences for society, and its rootedness in a problematic feature of human anthropology (the will to power), and finally tried to understand technology as such (its “essence”). The analytic approach, on the other hand, originally focused on a more science-based understanding of technology, its (mostly beneficial) potential for the progress of societies, and its rootedness in a rational (scientific) way to approach nature, and it finally tried to look not at technology as such but at specific problems or specific types of technologies.

In the continental philosophy of technology, technology is often interpreted as closely linked to a certain form of consciousness, a form of approaching nature (and also human interaction) from a perspective that is rooted in a scientific understanding of the world, which itself is rooted in the will to dominate nature. This approach is seen to replace or at least to endanger a value-based approach to reality. In this sense, Edmund Husserl’s phenomenology regards science and technology as a mere abstraction from the fullfledged real experience of the world we live in. In this way, the sphere of technical knowledge is limited and needs to be guided by value decisions, which do not have their basis in scientific or technical knowledge, but stem from our ethical knowledge of our life-world.

While technology is not at the center of Husserl’s interest, José Ortega y Gasset (1914/1961) was one of the first philosophers who aimed at a deeper understanding of the relation between human nature and technology. Rejecting Husserl’s later emphasis on the transcendental subject, he insists that human nature can only be understood by the formula “I am I plus my circumstances.” Philosophy can thus neither start from the isolated subject (as in idealism), nor can it interpret everything from the perspective of the material conditions (as in materialism). Rather, it must find a middle ground. The essence of humans is for Ortega not determined by nature; this distinguishes humans from plants or animals or from physical objects—all having a defined, specific given nature. Man must determine his own nature by himself by way of the creative imagination. Technology is interpreted as the material realization of this self-image; it is a projection of an inner invention into nature. According to Ortega, technology evolved in three phases: It started as a collection of accidental findings of means toward ends by pure chance. In a later state, these findings became traditions and skills that were passed on to the next generation. Modern technology marks a radical difference, since it is based on a systematic scientific approach, which forms the third phase. This approach, however, tends to become the dominant mode of thinking, so that man’s creative capacity for imagination (which is at the heart of man’s very essence) is in danger of being replaced or losing its importance (Ortega y Gasset, 1914/1961).

Martin Heidegger’s (1977) analysis of technology in his essay “The Question Concerning Technology” is also very influential. His philosophy aims at understanding the notion of being, which—so claims Heidegger—has been misinterpreted or neglected by traditional European philosophy. Since man is the only known being that can ask for the meaning of being, Heidegger’s analysis in Sein und Zeit starts from an interpretation of the existence of such a being ( Da-sein ). Even though his book is meant to be an exercise in philosophical (fundamental) ontology, it offers many anthropological insights about the specific human form of existence, in which the knowledge and the denial of one’s own mortality form essential human features.

In his later work, Heidegger (1977) understands technology as a specific form of disclosing reality. Asked for the essence of technology, people usually refer to it as a means to achieve an end (instrumental definition), or they define technology as an essential human activity (anthropological definition). Even though Heidegger admits that these definitions are “correct,” they do not disclose the essential truth about technology for two reasons. Essentially, (1) technology is not a tool for achieving an end, but rather the perspective under which everything that exists is seen only as a potential resource to achieve an (external) end. Furthermore, (2) this disclosure of reality is not a human-directed practice: Humans are driven objects rather than being themselves the active subjects. According to these conclusions, the instrumental and the anthropological definitions of technology do not capture the whole truth of technology. Let us look at these two points in turn, as follows:

• The essence of technology lies, according to Heidegger, in its capacity to disclose reality ( entbergen ) under a very specific, limited perspective. This perspective reduces everything to a potential object for manipulation, a resource ( Bestand ) for further activity. Technology is thus a way to disclose something hidden. Following his analysis of the Greek word for truth ( aletheia ) as referring to something undisclosed, he sees thus a “truth” at work, under which reality presents itself as a mere collection of resources for external purposes, rid of all inner logic and teleology that was so prominent in traditional understandings of nature. Heidegger points at the different ways in which a river is seen by a poet in an artwork ( Kunst werk), on the one hand, and, on the other hand, in which the same river is seen by an engineer as a potential resource for energy generation in a power plant ( Kraft werk).
• Heidegger then goes on to claim that opposed to the image of man being in control of technology and using it for his purposes, he should rather be seen as being provoked ( herausgefordert ) by this coming to pass. Heidegger clearly wants to reject the optimistic idea of “man being in control” through the help of modern technology and, rather, revert it to its opposite: man being driven by a force greater than himself. He calls this driving force the essence of technology, the en-framing ( Ge-stell ) that prompts humans to look at nature under the idea of its usability. In doing so, man is in highest danger, but not because of potential hazards or specific negative consequence of modern technology. The danger is, rather, that he loses sight of understanding nature in a different way and that he might finally end up understanding also himself and other humans only as potential “resources” or potential material for manipulation and instrumentalization. Heidegger suspects that art might be a potential antidote to this development: In Greek, techne originally encompassed also the production of beautiful objects in art. Thus, a deeper understanding of technology might reveal its relation to art and might point to the fact that art offers a potential answer to the challenge that modern technology poses to human self-understanding.

Certainly, Heidegger’s contribution to the modern philosophy of technology lies more in highlighting this essential dimension of technology as a threat, rather than in elaborating strategies to counter these inherent dangers. Heidegger’s article is arguably the single most influential essay written in the philosophy of technology, although his mannered, often dark language allows for different interpretations and often lacks the clarity of philosophical contributions from the analytical school. But the idea that “technology” and technological rationality is a limited form of looking at reality—one that is in strong need of a countervision, and that might further lead to a deformation of intersubjective human relations and that finally affects human self-understanding—has ever since been a prominent topic in different thinkers from Adorno and Marcuse to Jürgen Habermas, as illustrated earlier. This idea has often been linked with an ethical concern: Modern technology calls for new ethical guidelines, and despite some beneficial consequence, poses a potential threat to human existence. Much of this ethical debate about modern technology was triggered by its potential to radically destroy human life, be it through nuclear, biological, or chemical weapons or by consequences of environmental pollution and climate change.

Heidegger’s pupil Hans Jonas (1984) was one of the first philosophers to emphasize the need for a specific “ethics for the age of technology,” feeling that modern technology urges us to radically reconsider our ethical intuitions in order to meet the new challenges. Nevertheless, based on humans’ anthropological need to seek protection against nature, classical technology never fully reached this aim. Nature remained always more powerful than men, and the consequences of human actions were mostly not far-reaching. Traditional ethics could therefore focus on the “near and dear.” Modern technology, however, radically changes the picture: Its scope is unknown in premodern times; its consequences and potential dangers could be fatal, far-reaching, and irreversible. Focusing on the environmental problems of modern societies with, as the darkest perspective, the possible extinction of humankind, Jonas suggests broadening the scope of our ethical obligations: If our actions are more far-reaching than ever before in the history of humankind, we need to acquire a new ethical countervision. Jonas finds this remedy in the anthropological feature of our feelings of responsibility. Responsibility often expresses an asymmetrical relation, as in parents who feel responsible to care for their children. The old ethical intuition to derive obligations from the rights of free and conscious individuals, able to participate in argumentation and democratic decisions, seems to be too narrow to account for most environmental problems: Future generations are not yet born, animals and nature cannot in the same sense be regarded as having rights, as has been established in previous ethical approaches to the idea of universal human rights. But obligations may also stem from the idea of responsibility, from the idea that something has been given into our care.

Analytic philosophy is rooted in the quest for clear conceptualization, sound argumentation, and scientific precision. For early analytical philosophy in the Vienna Circle, the mathematical nature of scientific knowledge could serve as a role model for knowledge as such: hence, the need for and the extended usage of logical formalization within analytic philosophy. Skeptical of the quest to address the essence of things like “the technology” in general, analytic philosophers very often focus on concrete problems linked to very specific technologies. Even though many thinkers in the line of logical positivism thus greeted scientific knowledge as the highest form of knowledge, this did not always lead to an unbalanced embrace of technology. In Bertrand Russell (1951), we find a skeptical attitude toward the social benefits of technology, especially if it is linked with totalitarian ideology. Thus, he stresses the importance of democratic education; if placed in a democratic context and applied in well-defined careful steps, technology is, however, beneficial for progress in a way in which Karl Popper (1957) typically advertises as piecemeal social engineering. Important early contributions to an analytic philosophy of technology stem further from Mario Bunge (1979), whose ideas closely link to the program of logical empiricism and oppose the “romantic wailings about the alleged evils of technology” (p. 68).

Even though this distinction between humanities’ philosophy of technology and engineering’s philosophy of technology (Mitcham, 1994) marks the background of the philosophical discussion on technology in the early 20th century, the debate soon moved beyond this opposition. Three tendencies seem to be of importance.

First, continental philosophy was moving away from the attempt to come up with metaphysical, religious, or anthropological answers to the big questions. With the emergence of postmodernism, the alleged end of the “big stories” was proclaimed, thus making a metaphysical approach less fashionable. Appealing to ontology (as in Heidegger), to metaphysics, or to religious ideals (as in Jonas) seemed less promising. Even though early continental philosophy was very critical with regard to strategic rationality and technology, it has been criticized by postmodernism as not moving radically beyond the central modernistic Western ideal of a rational philosophical synthesis or universal world interpretation.

Second, the focus within the philosophy of technology moved toward a renewed interest in looking at concrete technologies and the challenges they pose for analytical and ethical reflection, a movement that has been called the empirical turn in the philosophy of technology (Kroes, 2001).

Third, different attempts were soon made to bridge the gap between the two camps. In post-world-war Germany, the Society of German Engineers (VDI) established a dialogue about the responsibilities of scientists and engineers, addressing topics and worries of the humanities. The experience of the massive and systematic use of technology for organized mass murder during the holocaust and the development of technology for modern warfare, including the development of the nuclear bomb, raised issues about the responsibilities of engineers. The debate of the VDI meetings resulted in a series of important publications on the philosophy of technology (Rapp, 1981); these must be recognized as an important attempt to synthesize different strands of philosophical thinking, even though it can be asked how far the VDI school was really successful in transcending its engineering-philosophical origins (Mitcham, 1994, p. 71).

Along a similar line, authors have tried to combine the phenomenological approach with American pragmatism, thus bridging insights of a more continental and a more analytical tradition. Common to phenomenology and pragmatism is the idea of the priority of praxis over theory and thus the tendency not to see technology as applied science but, rather, science as a purified or abstract form of (technological) praxis. Following the works of John Dewey, thinkers like Paul T. Durbin (1992), Larry Hickman (1990), and Don Ihde (1979) have tried to establish a pragmatist phenomenological approach to technology. The insights of Don Ihde that each technology either extends human bodily experience (e.g., the microscope) or calls for human interpretations (e.g., the thermometer) are of particular anthropological interest. If technology amplifies our experience, then it always does so at the cost of a reduction: In highlighting or amplifying certain aspects of reality, it makes invisible other aspects of this very same reality (as in an ultrasonic picture) (Ihde, 1979). The way technology thus “mediates” our interpretation of the world, and our actions within it, has been a further object of extended research (e.g., Verbeek, 2005).

A further attempt to bridge humanist and engineering tradition has been made by Carl Mitcham (1994), who nevertheless tries to defend the priority of the humanist perspective, but at the same time develops an analytic framework that should serve for further investigation within the philosophy of technology. He distinguishes among technology as object (tools), as type of knowledge, as activity, and as volition (expression of man’s intention or will). The 1980s and 1990s saw an increased interest, especially in the analyses of the first three aspects of this distinction.

With regard to the fourth aspect, ethical issues have been a central topic for many philosophers of technology, ranging from debates about the responsibility of scientists and engineers, medical and bioethics, business ethics, technology assessment, risk assessment and decision under uncertainty, to environmental ethics. Two of these fields are of particular interest from an anthropological perspective: In environmental ethics, those theories might shed light on anthropological questions seeking to interpret the environmental crisis as essentially rooted in human nature. It has been argued that it is a human tendency to value short-term (individual) interests more highly than long-term (collective) interests, thus putting a pessimistic neo-Hobbesian anthropology in the middle of the debate. According to Garrett Hardin (1968), it is this very human tendency (together with a mismatch in the growth of the human population that exceeds the growth of the supply of the food or other resources) that leads to the “tragedy of the commons.” Research in game theory and environmental sociobiology indicates the possibility of holding a more optimistic view of the development of cooperative strategies in humans (Axelrod, 1984), though the issue is still debated and there is room for a more pessimistic perspective, as has been defended early on by some sociobiologists (Dawkins, 1978) or recently by some philosophers (Gardiner, 2001).

In the ethical debate on transhumanism, finally, many links can be found to classical anthropological questions about the essence of man (e.g., Baillie, 2005; Fukuyama, 2004). The central debated question is whether it is morally allowed, forbidden, or even demanded from us to enhance our human capacities through new technologies, ranging from short-term nonevasive ways (like taking performanceenhancing drugs) to fundamental irreversible changes (like genetic engineering). While bioconservativists argue against an extended usage of enhancement technologies, transhumanists point to the potential benefits of these new options. It is reasonable to assume that these issues will be with us as technology advances and opens new possibilities to alter the human condition. This opens a radical new challenge to anthropology, which until recently dedicated itself to understanding the given human nature, while it now has to face the normative question of which we should choose as our future nature, once technology offers radical new options of changing human nature (e.g., as by slowing down or even stopping the process of aging). It seems that the anthropology of the future must take into consideration, more and more, normative claims and it must reach out to incorporate ethics to prepare itself for the challenges modern technology poses.

Looking at recent tendencies in research, it can be argued that the initial focus on linking technology with a universal, philosophical anthropological vision, also rooted in biological knowledge, was one of the key achievements of early philosophical anthropology in the works of Gehlen and others. What made these anthropologies remarkable was their attempt to bring together the different traditions of anthropological thought, ranging from philosophy to sociology and biology. A turn toward a more social perspective was established first by Gehlen himself, the Frankfurt school, and later STS studies, sometimes leading away from or even lacking both an underlying philosophical vision and an interest in our biological nature. Very recently, however, sociologists and philosophers have shown an increased interest in biology (as is visible in the ever-growing numbers of publications in sociobiology and the philosophy of biology). This increased attention has not yet led to a revival of an interest in the links between anthropology and technology. But in order to understand man—both in his evolutionary origins and (maybe even more) in his current historical situation—it seems to demand attention to man’s amazing capacity to develop technology.

It can reasonably be argued that what is thus needed is a new vision of how to synthesize the different fields of biological, social, and cultural anthropology. It seems that after the empirical turn to gather extended details over the biological and social aspects of technology, there is now a call for a new philosophical turn, seeking a new discourse synthesis. Many classical questions of anthropology will tend to remain unanswered, if academic research remains focused only on disciplinary perspectives, which always look at only a part of the whole picture. It is certainly true that man is a social animal, that he has biological roots and that he can ask ethical and philosophical questions about the good and about his place in this universe. The disciplinary separations in biology, sociology, and philosophy (to name just a few) tend, however, to distract from the fact that man in reality is a unity, meaning that a true answer to the most fundamental question of anthropology (What is man?) calls for a plausible combination of these approaches. To synthesize the different aspects of our knowledge about our own human nature is certainly far from being an easy task, but it seems more needed than ever.

But if this is not yet a big enough challenge, there is even a second aspect that makes the quest for a synthesis even more challenging. It seems that a new anthropological vision of humankind must answer a question that classical anthropology has not been dealing with: If technology soon allows us to alter our very nature, then we must know not only what the human condition is, but also what the human condition should be.

Ethics might again enter anthropological reflection, as has been hinted at already by early thinkers such as Scheler and Jonas. Recent attempts to place man in the middle of both a normative vision of ideals, on the one side, and against a profound overview of our descriptive knowledge about our essence, on the other side (as in the voluminous attempt at a synthesis in Hösle, 2004), deserve attention, as they might be the first steps toward a renewed synthetic anthropology that tries to bridge the gaps among the different disciplines. A deepened understanding of technology must be a central part of these efforts, since the way we use tools and produce artifacts is one of the remarkable features of humankind—a feature in much need of guidance by descriptive knowledge and ethical wisdom, especially in our age in which technology (of which humans have been the subject) is about to discover the condition humana as its potential object in a way more radical than ever before.

Bibliography:

• Adorno, T. (1979). Dialectic of enlightenment. London: Verso.
• Adorno, T. (1999). Aesthetic theory. London: Athlone.
• Ambrose, S. H. (2001). Paleolithic technology and human evolution. Science, 291 (5509), 1748–1753.
• Axelrod, R. (1984). The evolution of cooperation. New York: Basic Books.
• Baillie, H. (2005). Is human nature obsolete? Genetics, bioengineering, and the future of the human condition. Cambridge: MIT Press.
• Beck, B. (1980). Animal tool behaviour. New York: Garland.
• Berger, P. L., & Luckmann, T. (1966). The social construction of reality: A treatise in the sociology of knowledge. Garden City, NY: Anchor Books.
• Bunge, M. (1979). The five buds of technophilosophy. Technology in Society, 1 (1), 67–74.
• Castells, M. (1999). The information age: Economy, society and culture. Malden, MA: Blackwell.
• Dawkins, R. (1978). The selfish gene. Oxford, UK: Oxford University Press.
• Durbin, P. (1992). Social responsibility in science, technology, and medicine. Bethlehem, PA: Lehigh University Press.
• Fukuyama, F. (2004). Transhumanism. Foreign Policy, 144, 42–43.
• Gardiner, S. M. (2001). The real tragedy of the commons. Philosophy and Public Affairs, 30 (4), 387–416.
• Gehlen, A. (1980). Man in the age of technology. New York: Columbia University Press.
• Gehlen, A. (1988). Man, his nature and place in the world. New York: Columbia University Press.
• Habermas, J. (1971). Knowledge and human interests. Boston: Beacon Press.
• Haferkamp, H. (1992). Social change and modernity. Berkeley: University of California Press.
• Heidegger, M. (1977). The question concerning technology, and other essays. New York: Harper & Row.
• Hickman, L. (1990). John Dewey’s pragmatic technology. Bloomington: Indiana University Press.
• Hösle, V. (2004). Morals and politics (S. Randall, Trans.). Notre Dame, IN: University of Notre Dame Press.
• Ihde, D. (1979). Technics and praxis. Boston: D. Reidel.
• Jonas, H. (1984). The imperative of responsibility: In search of an ethics for the technological age. Chicago: University of Chicago Press.
• Kroes, P. (2001). The empirical turn in the philosophy of technology. New York: JAI.
• Laland, K. (2009). The question of animal culture. Cambridge, MA: Harvard University Press.
• Latour, B. (1979). The social construction of scientific facts. Beverly Hills, CA: Sage.
• Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. New York: Oxford University Press.
• Marx, K. (1938). London: Allen & Unwin.
• McDermott, J. (1969). Technology: The opiate of the intellectuals. The New York Review of Books, 13 (2), 25–35.
• Misa, T. J., Brey, P., & Feenberg, A. (2004). Modernity and technology. Cambridge: MIT Press.
• Mitcham, C. (1994). Thinking through technology: The path between engineering and philosophy. Chicago: University of Chicago Press.
• Nora, S. (1980). The computerization of society: A report to the president of France. Cambridge: MIT Press.
• Oakley, K. (1957). Man the tool-maker. Chicago: University of Chicago Press.
• Ortega y Gasset, J. (1961). Meditations on Quixote. New York: Norton. (Original work published 1914)
• Pfaffenberger, B. (1988). Fetishised objects and humanized nature: Towards an anthropology of technology. Man, 23 (2), 236–252.
• Pfaffenberger, B. (1992). Social anthropology of technology. Annual Revue of Anthropology, 21, 491–516.
• Pressnell, L. (1960). Studies in the industrial revolution, presented to T. S. Ashton. London: University of LondonAthlone Press.
• Rapp, F. (1981). Analytical philosophy of technology. Boston: D. Reidel.
• Russell, B. (1951). The impact of science on society. New York: Columbia University Press.
• Schaik, C. P., Deaner, R. O., & Merrill, M.Y. (1999).The conditions for tool use in primates: Implications for the evolution of material culture. Journal of Human Evolution, 36 (6), 719–741.
• Scheler, M. (1961). Man’s place in nature. New York: Farrar, Strauss & Giroux.
• Smelser, N. (1969). Social change in the industrial revolution: An application of theory to the British cotton industry. Chicago: University of Chicago Press.
• Snow, C. (1959). The two cultures and the scientific revolution (The Rede lecture, 1959). Cambridge, UK: Cambridge University Press.
• Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press.
• Verbeek, P. P. (2005). What things do: Philosophical reflections on technology, agency, and design. University Park: Pennsylvania State University Press.
• Winner, L. (1980). Do artifacts have politics? Daedalus, 109, 121–123.
• Winner, L. (1986). Technology as forms of life. In The whale and the reactor: A search for limits in an age of high technology. Chicago: University of Chicago Press.
• Woolgar, S. (1991). The turn to technology. Science, Technology and Human Values, 16 (1), 20–50.
• Wrangham, R. (1994). Chimpanzee cultures. Cambridge, MA: Harvard University Press.

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What Is Research, and Why Do People Do It?

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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What is a research paper?

A research paper is a paper that makes an argument about a topic based on research and analysis.

Any paper requiring the writer to research a particular topic is a research paper. Unlike essays, which are often based largely on opinion and are written from the author's point of view, research papers are based in fact.

A research paper requires you to form an opinion on a topic, research and gain expert knowledge on that topic, and then back up your own opinions and assertions with facts found through your thorough research.

➡️ Read more about  different types of research papers .

What is the difference between a research paper and a thesis?

A thesis is a large paper, or multi-chapter work, based on a topic relating to your field of study.

A thesis is a document students of higher education write to obtain an academic degree or qualification. Usually, it is longer than a research paper and takes multiple years to complete.

Generally associated with graduate/postgraduate studies, it is carried out under the supervision of a professor or other academic of the university.

A major difference between a research paper and a thesis is that:

• a research paper presents certain facts that have already been researched and explained by others
• a thesis starts with a certain scholarly question or statement, which then leads to further research and new findings

This means that a thesis requires the author to input original work and their own findings in a certain field, whereas the research paper can be completed with extensive research only.

➡️ Getting ready to start a research paper or thesis? Take a look at our guides on how to start a research paper or how to come up with a topic for your thesis .

Frequently Asked Questions about research papers

Take a look at this list of the top 21 Free Online Journal and Research Databases , such as ScienceOpen , Directory of Open Access Journals , ERIC , and many more.

Mason Porter, Professor at UCLA, explains in this forum post the main reasons to write a research paper:

• To create new knowledge and disseminate it.
• To teach science and how to write about it in an academic style.
• Some practical benefits: prestige, establishing credentials, requirements for grants or to help one get a future grant proposal, and so on.

Generally, people involved in the academia. Research papers are mostly written by higher education students and professional researchers.

Yes, a research paper is the same as a scientific paper. Both papers have the same purpose and format.

A major difference between a research paper and a thesis is that the former presents certain facts that have already been researched and explained by others, whereas the latter starts with a certain scholarly question or statement, which then leads to further research and new findings.

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Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Study explains why the brain can robustly recognize images, even without color

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Even though the human visual system has sophisticated machinery for processing color, the brain has no problem recognizing objects in black-and-white images. A new study from MIT offers a possible explanation for how the brain comes to be so adept at identifying both color and color-degraded images.

Using experimental data and computational modeling, the researchers found evidence suggesting the roots of this ability may lie in development. Early in life, when newborns receive strongly limited color information, the brain is forced to learn to distinguish objects based on their luminance, or intensity of light they emit, rather than their color. Later in life, when the retina and cortex are better equipped to process colors, the brain incorporates color information as well but also maintains its previously acquired ability to recognize images without critical reliance on color cues.

The findings are consistent with previous work showing that initially degraded visual and auditory input can actually be beneficial to the early development of perceptual systems.

“This general idea, that there is something important about the initial limitations that we have in our perceptual system, transcends color vision and visual acuity. Some of the work that our lab has done in the context of audition also suggests that there’s something important about placing limits on the richness of information that the neonatal system is initially exposed to,” says Pawan Sinha, a professor of brain and cognitive sciences at MIT and the senior author of the study.

The findings also help to explain why children who are born blind but have their vision restored later in life, through the removal of congenital cataracts, have much more difficulty identifying objects presented in black and white. Those children, who receive rich color input as soon as their sight is restored, may develop an overreliance on color that makes them much less resilient to changes or removal of color information.

MIT postdocs Marin Vogelsang and Lukas Vogelsang, and Project Prakash research scientist Priti Gupta, are the lead authors of the study, which appears today in Science . Sidney Diamond, a retired neurologist who is now an MIT research affiliate, and additional members of the Project Prakash team are also authors of the paper.

Seeing in black and white

The researchers’ exploration of how early experience with color affects later object recognition grew out of a simple observation from a study of children who had their sight restored after being born with congenital cataracts. In 2005, Sinha launched Project Prakash (the Sanskrit word for “light”), an effort in India to identify and treat children with reversible forms of vision loss.

Many of those children suffer from blindness due to dense bilateral cataracts. This condition often goes untreated in India, which has the world’s largest population of blind children, estimated between 200,000 and 700,000.

Children who receive treatment through Project Prakash may also participate in studies of their visual development, many of which have helped scientists learn more about how the brain's organization changes following restoration of sight, how the brain estimates brightness, and other phenomena related to vision.

In this study, Sinha and his colleagues gave children a simple test of object recognition, presenting both color and black-and-white images. For children born with normal sight, converting color images to grayscale had no effect at all on their ability to recognize the depicted object. However, when children who underwent cataract removal were presented with black-and-white images, their performance dropped significantly.

This led the researchers to hypothesize that the nature of visual inputs children are exposed to early in life may play a crucial role in shaping resilience to color changes and the ability to identify objects presented in black-and-white images. In normally sighted newborns, retinal cone cells are not well-developed at birth, resulting in babies having poor visual acuity and poor color vision. Over the first years of life, their vision improves markedly as the cone system develops.

Because the immature visual system receives significantly reduced color information, the researchers hypothesized that during this time, the baby brain is forced to gain proficiency at recognizing images with reduced color cues. Additionally, they proposed, children who are born with cataracts and have them removed later may learn to rely too much on color cues when identifying objects, because, as they experimentally demonstrated in the paper, with mature retinas, they commence their post-operative journeys with good color vision.

To rigorously test that hypothesis, the researchers used a standard convolutional neural network, AlexNet, as a computational model of vision. They trained the network to recognize objects, giving it different types of input during training. As part of one training regimen, they initially showed the model grayscale images only, then introduced color images later on. This roughly mimics the developmental progression of chromatic enrichment as babies’ eyesight matures over the first years of life.

Another training regimen comprised only color images. This approximates the experience of the Project Prakash children, because they can process full color information as soon as their cataracts are removed.

The researchers found that the developmentally inspired model could accurately recognize objects in either type of image and was also resilient to other color manipulations. However, the Prakash-proxy model trained only on color images did not show good generalization to grayscale or hue-manipulated images.

“What happens is that this Prakash-like model is very good with colored images, but it’s very poor with anything else. When not starting out with initially color-degraded training, these models just don’t generalize, perhaps because of their over-reliance on specific color cues,” Lukas Vogelsang says.

The robust generalization of the developmentally inspired model is not merely a consequence of it having been trained on both color and grayscale images; the temporal ordering of these images makes a big difference. Another object-recognition model that was trained on color images first, followed by grayscale images, did not do as well at identifying black-and-white objects.

“It’s not just the steps of the developmental choreography that are important, but also the order in which they are played out,” Sinha says.

The advantages of limited sensory input

By analyzing the internal organization of the models, the researchers found that those that begin with grayscale inputs learn to rely on luminance to identify objects. Once they begin receiving color input, they don’t change their approach very much, since they’ve already learned a strategy that works well. Models that began with color images did shift their approach once grayscale images were introduced, but could not shift enough to make them as accurate as the models that were given grayscale images first.

A similar phenomenon may occur in the human brain, which has more plasticity early in life, and can easily learn to identify objects based on their luminance alone. Early in life, the paucity of color information may in fact be beneficial to the developing brain, as it learns to identify objects based on sparse information.

“As a newborn, the normally sighted child is deprived, in a certain sense, of color vision. And that turns out to be an advantage,” Diamond says.

Researchers in Sinha’s lab have observed that limitations in early sensory input can also benefit other aspects of vision, as well as the auditory system. In 2022, they used computational models to show that early exposure to only low-frequency sounds, similar to those that babies hear in the womb, improves performance on auditory tasks that require analyzing sounds over a longer period of time, such as recognizing emotions. They now plan to explore whether this phenomenon extends to other aspects of development, such as language acquisition.

The research was funded by the National Eye Institute of NIH and the Intelligence Advanced Research Projects Activity.

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The Macroeconomic Impact of Climate Change: Global vs. Local Temperature

This paper estimates that the macroeconomic damages from climate change are six times larger than previously thought. We exploit natural variability in global temperature and rely on time-series variation. A 1°C increase in global temperature leads to a 12% decline in world GDP. Global temperature shocks correlate much more strongly with extreme climatic events than the country-level temperature shocks commonly used in the panel literature, explaining why our estimate is substantially larger. We use our reduced-form evidence to estimate structural damage functions in a standard neoclassical growth model. Our results imply a Social Cost of Carbon of $1,056 per ton of carbon dioxide. A business-as-usual warming scenario leads to a present value welfare loss of 31%. Both are multiple orders of magnitude above previous estimates and imply that unilateral decarbonization policy is cost-effective for large countries such as the United States.

Adrien Bilal gratefully acknowledges support from the Chae Family Economics Research Fund at Harvard University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Disappointment

In high school I took part in many math competitions; the hardest (and therefore to my teenage mind the only ones that counted) were the competitions relating to the international mathematical olympiad (IMO). In Melbourne there was a program of competition and training that culminated in a nine hour exam spread over two days to determine the makeup of the Australian IMO team. I remember very well the first time I took the exam. It was 1987, and the IMO was to be held that year in Cuba. As I sat at my carrel in the Morris library, I took the February sunlight for fortune smiling on me, inspiration spilled liberally from my Pelikano steel nib fountain pen, and I went home at the end of the second day in a blur of fatigue and self-congratulation. Six weeks later a pregnant manila envelope arrived in the mail. From its girth alone I knew I had aced the exam and won my rightful spot on the team. Before even opening the envelope I could see myself in the green woolen team blazer with the Australian coat of arms embroidered on the breast pocket, and by the time I found a letter opener I was shaking hands with Fidel Castro.

The envelope contained…sixty-odd sheets of loose-leaf paper, no invitation, no cover letter: my exam papers, bloodstained with question marks, lines through paragraphs, squiggles of uncomprehension, Xs and Os. My stomach fell. I blushed. In a fraction of a second I rewrote or recolored dozens of memories and fantasies from the recent past and future, and became intensely conscious of and embarassed by my vanity and foolishness. What I now find remarkable was the speed and scope of my transformation; the analogy that comes to mind is being struck by a speeding car.

So how does one deal with disappointment? Freud, in Civilization and Its Discontents identifies three typical measures:

powerful deflections, which cause us to make light of our misery; substitutive satisfactions, which diminish it; and intoxicating substances, which make us insensible to it.

If these are the typical responses, are there any others? Before trying to answer this it might be helpful first to articulate what disappointment actually is , and then to ask what it’s for . Evidently, disappointment is a form of mental suffering. It is so unpleasant that we can experience it in a host of physiological dimensions. Profound mental suffering involves a complex array of interactions between any number of processes and subsystems, both conscious and unconscious, involving both the brain and the limbic system. The suffering that arises from disappointment is that that accompanies disruption: disappointment causes a certain kind of shake-up or realignment of our worldview and self-image and consequently of our priorities; this disruption can be so great that we sometimes emerge from it a very different person.

According to certain schools of cognitive science (e.g., Minsky’s Society of Mind model) the idea of a “self” as a unified, indivisible entity is an oversimplification; rather (they suggest) a self is an uneasy federation of simpler subsystems (sometimes termed “agents”) with their own local goals and interests, which are frequently in competition with one another. Under ordinary circumstances stability is achieved by a complicated system of temporary alliances, detentes, three-way standoffs, and so forth. Our subjective sense of the unified self is—in itself!—also a source of stability. Sometimes a dramatic change in (real or perceived) external circumstances—an unforseen event, an unpleasant discovery—can lead to a cascade of disruptions to this order. This is the mechanism of disappointment, and why it is so painful; it is both a crisis and an opportunity—in Homer Simpson’s inspired terminology, a crisitunity.

Disappointment measures in pain the gap between reality and what we want the world to be. Disappointment matters. It matters because we don’t actually live in the real world. We live in our heads, in a mental world of assumptions, recollections, anticipations, desires and conjectures. And even when we do meet reality, it’s a mistake to think that what our senses feed us is objective, unfiltered, unsorted. Rather we operate according to an interrogative protocol—we ask the world questions to confirm what we already “know” (or, more accurately: hope), and only when we get an unpleasant surprise do we take a closer look. As Proust says,

The real voyage of discovery consists not in seeking new landscapes, but in having new eyes. Happiness is beneficial for the body, but it is grief that develops the powers of the mind.

In his famous paper, “How not to prove the Poincaré Conjecture,” John Stallings writes about an adventure in his mathematical life, how he discovered a proof of the Poincaré Conjecture but later found it to be mistaken. He goes so far as to describe this episode as a “sin;” but the sin was not in the mistake per se, rather it was his resistance to recognizing it as such. He writes,

There are two points about this incorrect proof worthy of note …(t)he second…is that I was unable to find flaws in my ‘proof’ for quite a while, even though the error is very obvious. It was a psychological problem, a blindness, an excitement, an inhibition of reasoning by an underlying fear of being wrong. Techniques leading to the abandonment of such inhibitions should be cultivated by every honest mathematician.

Few mathematicians are as honest or as generous as Stallings in sharing their own stories of disappointment. This is because disappointment often comes wrapped in shame, because our goals are inextricable from our personal and social attachments and relationships. Convention and social norms dictate that any display of human weakness or failing is “unprofessional.” We’re not supposed to admit it when we feel stupid, or underappreciated, or jealous, or that we cared so much about something that when it didn’t go our way we felt shattered. Techniques leading to the abandonment of such inhibitions should be cultivated by every honest mathematician.

Disappointment takes many forms; a partial list from my personal history includes:

being un- or under-acknowledged in a colleague’s paper or talk;

being scooped;

missing out on a job/prize/conference invitation;

having a prospective student work with someone else;

having a potential advisor turn me down as a student;

having a promising line of attack on a problem fail to pan out;

discovering an error in an amazing proof;

having a paper go unread or a book go unreviewed;

seeing an admired senior colleague behave badly;

realizing that I haven’t lived up to my own standards of behavior;

discovering that success, when it came, was not all I hoped it would be.

The last one, perhaps, deserves elaboration. Some acute disappointments in my career were the result of getting what I thought I wanted: a paper in a fancy journal; a job offer; tenure; an invitation to talk at a fancy conference. I don’t mean to diminish the value of such things at all, or the challenges (personal or structural) many people must overcome to achieve them; much about the way such “rewards” are distributed in academic culture is unfair, often in systematic ways, and it should be the goal of all of us to point this out and work to change it wherever we can. I also don’t mean to suggest that success has been joyless; the opposite is true. Nevertheless it is the case that sometimes when we get what we think we want, we discover that these things weren’t what we thought they were, and (more importantly) that we are not who we though we were. When disappointment accompanies success it is worth paying special attention to. If we get what we want but it doesn’t bring us fulfillment, then what’s really going on? In my experience, it has only been at the point of my posing this question that I have acquired insight, and the agency to really change things or come to terms with them.

It took a month of pain after the manila envelope arrived before curiosity got the better of me and I opened it again. And a remarkable thing happened. The exam pages: my answers, the blots, the corrections, the red ink, the comments, were exactly as before. But time and some strange alchemy of which disappointment itself was the catalyst had altered their meaning. An actual human being had taken the time to read my work and share valuable feedback with me. My annotated exam was no longer a certificate of failure, it was a how-to manual: it was about how to prove an inequality by leveraging the convexity of a cleverly chosen auxiliary function, or how to recast a geometric figure in terms of complex numbers and understand it with algebra. These math problems weren’t “problems” at all: they were windows into mathematics itself. And the manila envelope wasn’t a slap in the face, it was a gift; but to see it as a gift I had to see it with new eyes. I never got to Cuba, but I’d taken my first steps on a longer and far more interesting and rewarding journey that continues to this day.

Acknowledgment

I would like to thank Kathryn Kruse for her extensive feedback on and advice about an early draft of this essay.

Danny Calegari is a professor of mathematics at the University of Chicago. His email address is [email protected] .

Article DOI: 10.1090/noti2782

The Early Career Section offers information and suggestions for graduate students, job seekers, early career academics of all types, and those who mentor them. Krystal Taylor and Ben Jaye serve as the editors of this section. Next month’s theme will be Math and the Real World.

Photo of Danny Calegari is courtesy of Danny Calegari.

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