Bibliographic record
Abstract
Abstract: In this paper, we contend that there are two ways of arguing, namely sincere and insincere arguing. We draw such a distinction, based on the felicity conditions of the complex speech act of arguing as modelled in van Eemeren and Grootendorst’s pragma-dialectical approach. We introduce a conversa-tional setting, which contains a speech act of arguing that does not count as in-sincere arguing, while being a sui gene-ris form of sincere arguing. We desig-nate it as “cooperative inquiry”. Finally, we show that argument evaluation plays a key role in determining whether an in-stance of arguing counts as either argu-ing sincerely or insincerely. Résumé: Dans cet article, nous affir-mons qu’il existe deux manières d’argu-menter, à savoir l’argumentation sincère et l’argumentation non sincère. Nous établissons une telle distinction, basée sur les conditions de félicité de l’acte de parole complexe consistant à argu-menter, tel que modélisé dans l’ap-proche pragma-dialectique de van Eemeren et Grootendorst. Nous intro-duisons un cadre conversationnel, qui contient un acte de parole d'argumenta-tion qui n'est pas considéré comme une argumentation non sincère, tout en étant une forme sui generis d'argumentation sincère. Nous la désignons comme « enquête coopérative ». Enfin, nous montrons que l’évaluation des argu-ments joue un rôle clé pour déterminer si un cas d’argumentation compte comme une argumentation sincère ou non.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".