Bibliographic record
Abstract
Abstract: In this essay, I make a plea for a wide-ranging, open perspective on the evaluation of arguments. This involves a more flexible understanding of what fallacies are and for what argu-ments may be used. I acknowledge the great wealth of argumentation theory, but bemoan the lack of systematic, re-peatable, and explainable evaluation procedures. I then go on to introduce the works which contribute to this spe-cial issue and explain how they assist in the fulfilment of my hopes. Résumé: Dans cet essai, je plaide en faveur d’une perspective large et ou-verte sur l’évaluation des arguments. Cela implique une compréhension plus flexible de ce que sont les sophismes et des arguments qui peuvent être utilisés. Je reconnais la grande richesse de la théorie de l’argumentation, mais je dé-plore le manque de procédures d’éval-uation systématiques, reproductibles et explicables. Je présente ensuite les travaux qui contribuent à ce numéro spécial et explique comment ils con-tribuent à la réalisation de mes espoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.023 |
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".