Bibliographic record
Abstract
Normative theories of argumentation tend to assume that logical and dialectical rules suffice to ensure the rationality of argumentative discourse. Yet, in everyday debates people use arguments that seem valid in light of such rules but nonetheless biased and tendentious. This article seeks to show that the rationality of argumentation can only be fully promoted if we take into account its ethical dimension. To substantiate this claim, I review some of the empirical evidence indicating that people’s inferential reasoning is systematically affected by a variety of biases and heuristics. Insofar as these cognitive illusions are typically unintentional, it appears that arguers may be biased despite their well-intended efforts to follow the rules of critical argumentation. Nevertheless, I argue that people remain responsible for the rationality of their arguments, given that there are a number of measures that they can (and ought to) take to avoid such distortions. I highlight the importance of argumentational virtues and critical thinking to rational debates, and describe a set of indirect strategies of “argumentative self-control”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".