Bibliographic record
Abstract
En sciences sociales, seules des simulations peuvent nous donner une idée de la manière dont des formes collectives résultent d’interactions complexes. Mais plus elles sont complexes – en particulier en intégrant les effets des représentations des formes collectives par les acteurs – plus les simulations peuvent diverger, d’où une indétermination. Or nous sommes plus sensibles à ce qui est pour nous reconnaissable (formes plus stables, mieux différenciées). Combiner ces deux tendances nous amène à privilégier non pas une correspondance avec une réalité elle aussi complexe, mais la capacité des simulations à permettre entre elles des comparaisons qui puissent nous offrir des moyens de les critiquer les unes par les autres. Nous conservons à l’esprit, en parallèle, les scénarios divergents qui restent les plus différentiables tout en restant attentif à des divergences d’abord négligées.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".