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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.170 | 0.036 |
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; both teacher heads agree on what is shown here.
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".