Bibliographic record
Abstract
The purpose of this paper is twofold: to give a good account of the argument from ignorance, with a presumptive argumentation scheme, and to raise issues on the work of Walton, the nature of abduction and the concept of epistemic closure. First, I offer a brief disambiguation of how the terms 'argument from ignorance' and 'argumentum ad ignorantiam' are used. Second, I show how attempts to embellish this form of reasoning by Douglas Walton and A.J. Kreider have been unnecessary and unhelpful. Lastly, I offer a full and effective account of the argument from ignorance and discuss the lessons of the analysis.Le but de cet article est double: donner un bon compte rendu de l'argument par l'ignorance, avec un schème d'argumentation présomptif, et soulever des questions sur certains aspects de l’œuvre de Walton, la nature des raisonnements abductifs et le concept de fermeture épistémique. Premièrement, j'offre une brève désambiguïsation de la façon dont les termes «argument par l'ignorance» et «argumentum ad ignorantiam» sont utilisés. Deuxièmement, je montre comment les tentatives de Douglas Walton et de A.J. Kreider d'embellir cette forme de raisonnement ont été ni nécessaires et ni utiles. Enfin, j'offre un compte-rendu complet et utile de l'argument par l'ignorance et je discute des leçons de l'analyse.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".