Bibliographic record
Abstract
Afin de débarrasser la causalité de son inutile cortège positiviste, il faut analyser les pratiques des chercheurs qui produisent et traitent des données dans une perspective causale, pour en dégager le noyau significatif. On découvrira alors (1) que ce noyau de l'analyse causale constitue toujours une approche essentielle en sciences sociales, (2) approche qui peut s'accommoder aussi bien de données qualitatives que quantitatives, (3) à la condition expresse qu'on l'utilise dans une perspective heuristique. L'analyse causale peut en effet fournir une sténographie du social, qui enregistre et interprète les traces de processus dans lesquels sont engagés les acteurs qui vivent ces rapports. Cette heuristique doit se définir comme un humble travail d'elucidation progressive de ces processus, qui repose sur une variété de modèles de construction des données : données quantitatives ou qualitatives, transversales ou longitudinales, micro- ou macro-sociologiques.
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 | 0.072 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".