Bibliographic record
Abstract
Abstract: This paper addresses the prob-lem of how to identify and evaluate argu-ments made in a nonverbal form. Such arguments may employ images, sounds, or a combination of these in a truly mul-timodal presentation. Here, we concen-trate on those which are classified as au-ditory, i.e. contain at least one premise or the conclusion in sound form. We pro-pose and test a solution whereby some el-ements of the Comprehensive Assess-ment Procedure for Natural Argumenta-tion (CAPNA) are modified to allow for the evaluation of auditory arguments. The results of this approach are illus-trated with the help of a number of au-thentic examples. Résumé: Cet article aborde le problème de la façon d'identifier et d'évaluer les ar-guments présentés sous une forme non verbale. De tels arguments peuvent uti-liser des images, des sons ou une com-binaison de ceux-ci dans une présenta-tion véritablement multimodale. Ici, nous nous concentrons sur les arguments classés comme auditifs, c'est-à-dire qui contiennent au moins une prémisse ou la conclusion sous forme sonore. Nous pro-posons et testons une solution dans laquelle certains éléments de la procédure d'évaluation globale de l'argu-mentation naturelle (CAPNA) sont mod-ifiés pour permettre l'évaluation des ar-guments auditifs. Les résultats de cette approche sont illustrés à l’aide de nom-breux exemples authentiques.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.029 |
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".