Bibliographic record
Abstract
Abstract: This paper aims to develop the criteria for assessing semantic arguments. However, while this notion constituted the core of ancient dialectics and is addressed in several approaches to argument analysis, the criteria for evaluating such arguments are insufficient. This paper intends to address this problem by combining the insights of classical and contemporary logic and testing them against some controversies involving controversial definitions or classifications. Through detailed case studies of the argumentative uses involving the (re)definitions of racism, war, peace, and feminism, we formulated and tested eight evaluation criteria that may be expressed as critical questions. Résumé: Cet article vise à développer les critères d’évaluation des arguments sémantiques. Cependant, bien que cette notion constitue le coeur de la dialectique ancienne et soit abordée dans plusieurs approches de l’analyse des arguments, les critères d’évaluation de ces arguments sont insuffisants. Cet article vise à résoudre ce problème en combinant les idées de la logique classique et contemporaine et en les testant par rapport à certaines controverses impliquant des définitions ou des classifications controversées. À travers des études de cas détaillées sur les usages argumentatifs impliquant les (re)définitions du racisme, de la guerre, de la paix et du féminisme, nous avons formulé et mis à l’épreuve huit critères d'évaluation qui peuvent être exprimés sous forme de questions critiques.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".