Toutes les relations sont internes — la nouvelle version
Bibliographic record
Abstract
Kevin Mulligan a introduit la distinction entre les descriptions épaisses et minces dans la philosophie des relations. Cette distinction lui a permis d’affirmer les thèses suivantes : toutes les relations sont « minces » et internes, et aucune n’est « épaisse » et externe. J’accepte et j’utilise la distinction de Mulligan entre mince et épais afin de soutenir que ce ne sont pas toutes les relations internes qui sont minces. Il existe également des relations internes épaisses, et celles-ci abondent en physique mathématique. Je soutiens en outre qu’il peut y avoir des relations externes minces. Cependant, en introduisant une distinction entre relations fortement et faiblement internes, je suis d’accord pour affirmer avec Mulligan que toutes les relations fortement internes sont des relations minces.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".