Bibliographic record
Abstract
Abstract: To get a better comprehension of the nature of argumentation, we need to understand the context in which this practice produces its particular benefits. I hold that this context consists basically in the presence of two conditions: 1) the need for cooperation, and 2) the possibility of dissent. I argue that contributing to the coordination of collective action is the particular benefit argumentation is able to produce in this context and that obtaining this benefit constitutes its main function. Thus, the main function of argumentation is not epistemic. Truth is important when we argue, but epistemic improvement is not the main aim of argumentation, even if this benefit may be a common result of it. Résumé: Pour mieux comprendre la nature de l'argumentation, il est nécessaire de comprendre le contexte dans lequel cette pratique produit ses bénéfices particuliers. Je soutiens que ce contexte repose essentiellement sur la présence de deux conditions : 1) le besoin de coopération ; 2) la possibilité de dissidence. Je soutiens que contribuer à la coordination de l'action collective est le bénéfice particulier que l'argumentation est capable de produire dans ce contexte et que l'obtention de ce bénéfice constitue sa fonction principale. Ainsi, la fonction principale de l'argumentation n'est pas épistémique. La vérité est importante lorsque nous argumentons, mais l'amélioration épistémique n'est pas l'objectif principal de l'argumentation, même si ce bénéfice peut en être un résultat courant.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".