Bibliographic record
Abstract
Abstract: Normative theories of argumentation tend to assume that logical and dialectical rules suffice to ensure the rationality of debates. Yet empirical research on human inference shows that people system-atically fall prey to cognitive and motivational biases which give rise to various forms of irrational reason-ing. Inasmuch as these biases are typically unconscious, arguers can be unfair and tendentious despite their genuine efforts to follow the rules of argumentation. I argue that arguers remain nevertheless respon-sible for the rationality of their rea-soning, insofar as they can (and ar-guably ought to) counteract such biases by adopting indirect strategies of argumentative self-control. Résumé: Les théories normatives de l’argumentation tendent à présumer que les règles de la logique et de la dialectique suffisent pour assurer la rationalité du discours argumentatif. Pourtant, la recherche empirique sur l’inférence humaine montre que nous sommes souvent affectés par des biais cognitifs et motivationnels qui conduisent à diverses formes de raisonnement irrationnel. Etant don-né que ces biais sont inconscients, chacun peut se montrer tendancieux en dépit de l’effort pour respecter les règles d’argumentation. Je soutiens que chacun demeure néanmoins res-ponsable de la rationalité de ses rai-sonnements, dans la mesure où l’on peut neutraliser ces biais moyennant certaines stratégies d’autocontrôle argumentatif.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".