Bibliographic record
Abstract
Abstract: Michael Gilbert challenges established norms in argumentation theory by introducing a multi-modal framework that incorporates emotive, visceral, and kisceral dimensions alongside logical modes in constructing arguments. This article critically assesses Gilbert’s multi-modal argument framework, highlighting his departure from the rational structure of arguments. Gilbert proposes distinct evaluation criteria for different argument forms, a deviation from traditional rational frameworks. To address this discrepancy within Gilbert’s framework, this paper advocates a middle ground. This position aims to appreciate extra-logical elements present in arguments while maintaining fidelity to logical structures. Bertrand Russell’s notion of knowledge by acquaintance is utilized to construct this intermediary standpoint. Résumé: Michael Gilbert remet en question les normes établies en théorie de l'argumentation en introduisant un cadre multimodal intégrant les dimensions émotionnelle, viscérale et kiscérale, ainsi que les modes logiques, dans la construction des arguments. Cet article évalue de manière critique le cadre argumentatif multimodal de Gilbert, soulignant son écart par rapport à la structure rationnelle des arguments. Gilbert propose des critères d'évaluation distincts pour différentes formes d'argumentation, s'écartant ainsi des cadres rationnels traditionnels. Pour remédier à cette divergence au sein du cadre de Gilbert, cet article préconise une position intermédiaire. Cette position vise à apprécier les éléments extra-logiques présents dans les arguments tout en préservant la fidélité aux structures logiques. La notion de connaissance non propositionnelle, directe, et immédiate de Bertrand Russell est utilisée pour construire ce point de vue intermédiaire.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".