Bibliographic record
Abstract
Lecture 9 tackles the notion of persuasion when using formal and informal arguments. Based on inductive reasoning, informal arguments aim to persuade listeners of their truth by the sheer weight of the reasons the presenter can mobilize. Unlike formal arguments, informal arguments are precisely the sorts of arguments that are wedded to the idea of truth. They are the kinds of arguments where rhetoric is most called for. Le cours 9 traite de la notion de persuasion dans des circonstances où l’on a recours à des arguments formels et informels. Basés sur un raisonnement inductif, les arguments informels tentent de persuader l’auditeur de leur vérité par la simple force des raisons que le présentateur peut rassembler. Contrairement aux arguments formels, les arguments informels représentent précisément le type d’argument qui adhère à l’idée de vérité. Ils sont le type d’argument où la rhétorique peut être le plus utile.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".