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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".