Bibliographic record
Abstract
Kim attribue aux émergentistes un modèle de « réduction logique » dans lequel la prédiction ou l’explication d’une occurrence de la propriété réduite ne requiert, outre des informations sur le niveau réducteur, que des principes logiques et mathématiques. Sur la base de cette interprétation, je conteste deux thèses de Kim. La première concerne la légitimité du modèle émergentiste de réduction. J’essaie de montrer, à l’aide de l’exemple de l’addition des masses, que l’adoption de la réduction logique rendrait irréductibles certaines propriétés qui sont clairement réductibles. La deuxième est la thèse selon laquelle la réduction fonctionnelle correspond aux exigences émergentistes sur la réduction. Telle que Kim la caractérise, la réduction fonctionnelle comporte, outre une définition fonctionnelle de la propriété à réduire, l’indication des propriétés réalisatrices. Or cette information, qui correspond à la découverte d’une loi de correspondance (locale), est empirique et non seulement logique.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".