Argumentation theory | Communication and Mass Media | Research Starters | EBSCO Research

<p>Argumentation theory explores the processes and methods of reasoning and debate used by individuals in both formal and informal contexts. The theory has roots in ancient philosophical discourse, particularly from figures like Aristotle, and has evolved through the contributions of modern philosophers such as Chaïm Perelman and Stephen Toulmin. It highlights how arguments are structured, identifying key components such as claims, grounds (or data), and warrants, which collectively help participants make their case. </p> <p>Additionally, arguments can be categorized into three main types: factual claims, which are verifiable; judgment or value claims, which are subjective; and policy claims, which pertain to proposed courses of action. This framework acknowledges the influence of personal biases, often shaping the reasoning process, and emphasizes the importance of logical support, backing, qualifiers, and rebuttals in strengthening arguments. In academic contexts, the theory suggests that creating valid topics should focus on policy arguments, while also addressing counterarguments to foster a comprehensive debate. Overall, argumentation theory serves as a critical tool for understanding how reasoning and persuasive communication function in various scenarios.</p>

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

#predictingthefuture #newfutureofwork | Jaime Teevan

🌱 Prediction: Knowledge will outgrow publication. We’re already seeing academic publication start to buckle under AI, sometimes absurdly. I still publish research more or less the way Darwin did. I run a study, write it up, a few other scientists check it over, and the result gets filed away as a document with my name on the front. Faster than Darwin, with better figures, but the same basic shape. I predict that shape won’t last another decade. Academic authors are starting to slip hidden instructions into papers to flatter the AI that might review them. Reviewers are spending time checking whether citations exist or were hallucinated. Researchers asking AI to tell them about a paper instead of reading it directly. These are signs that the creation of new knowledge is outgrowing the articles that used to contain it. An academic paper serves many purposes at once. It makes an argument legible. It lets strangers check one's reasoning. It assigns credit and responsibility. It records who knew what and when. A paper was the only container we had for these different jobs, so it carried all of them together. With AI, they can be separated. My guess is that means the unit of publication will get smaller. Much of my research has focused on microproductivity, developing the idea that large accomplishments can be built from many small contributions. Publication will start to become a form of microproductivity. Instead of holding onto a result until it can be wrapped in a narrative large enough to justify a paper, researchers will publish it the moment it’s solid. Each finding, method, or negative result will be citable and carry its own provenance, so credit and reasoning travel with it. Reviewing will shrink to match, so claims get checked as they’re made instead of in one verdict at the end. But more than changing publication, the deeper change will be to how research itself is done. You may have heard the term “compound engineering,” where every bug fixed, evaluation written, workflow documented, or lesson learned becomes part of the system’s memory. I predict we’re about to see “compound science,” where every experiment, evaluation, insight, artifact, and learned capability becomes a reusable asset for future discovery. Findings will become evidence. Methods will become building blocks. Failed approaches will become constraints. For centuries, science has relied on humans to navigate an ever-growing body of knowledge. Soon that body of knowledge will help navigate itself. Scientists will spend less time searching for hypotheses and more time deciding which opportunities to pursue. AI systems will propose explanations, design experiments, run analyses, and explore many possibilities in parallel. Every discovery will become a part of the machinery that produces the next one. Papers ten years from now will look less like my current papers than my current papers look like Darwin’s. If they exist at all. #PredictingTheFuture #NewFutureOfWork

Andreas De Block on Twitter / X

It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026

ViewSift

The web's evidence-based discussion platform. Built on ATProto!

ViewSift

The web's evidence-based discussion platform. Built on ATProto!

Fact checking Moravec's paradox

This famous aphorism is neither true nor useful

fenc.es — be wrong on the internet, productively

Trace the map of reasonable disagreement. Break arguments into statements, rate confidence and importance, and find the crux.

Counting Arguments and AI

A “counting argument” is a style of argument common among creationists, who argue that the theory of evolution cannot be true and therefore humans (and usually animals too) were made in basically their present form by God.

Most arguments scatter across papers, posts, and threads — and evaporate. A claim tree gives them a stable structure to gather against, the way a cathedral gathers centuries of work into a single, standing thing.

Google DeepMind Paper Argues LLMs Will Never Be Conscious

Philosophers said the paper’s argument is sound, but that “all these arguments have been presented years and years ago.”

How to counter "everything is a construct" worldview?

Many of the people in humanities I encounter seem very wedded to mindset I refer to as "construct = everything". For example, they say bazar things such as gender is 100% a construct. They say...

Konrad Hinsen's blog

How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it.