Bibliographic record
Abstract
Abstract: The paper presents an extended scheme for the inference to the best explanation (IBE). The scheme precisely treats the epistemic modifiers (“hypothetically,” “plausibly,” “presumably”) of the inference, acknowledges its contrastive nature, clarifies the logical support between premises and conclusions (linked, convergent, and serial support), and introduces additional premises essential for inferring justified conclusions (especially those related to causal explanations and more demanding standards of proof). Overall, it advances the existing schemes for IBE in argumentation theory and treats IBE as a par excellence argumentative, rather than explanatory, form of reasoning. Résumé: L’article présente en détail un schéma pour l’inférence vers la meilleure explication (IME). Le schéma traite précisément les modificateurs épistémiques (« hypothétiquement », « plausiblement », « vraisemblablement ») de l’inférence, reconnaît sa nature qui fait contraste, clarifie l’appui logique entre les prémisses et les conclusions (l’appui lié, convergent et sériel) et introduit des prémisses supplémentaires essentielles pour inférer des conclusions justifiées (en particulier celles liées aux explications causales et aux normes de preuve plus exigeantes). Dans l’ensemble, l’article fait progresser les schémas existants pour l’IME dans la théorie de l’argumentation et traite l’IME comme une forme de raisonnement argumentatif par excellence, plutôt qu’explicatif.
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 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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".