Recentrer l’analyse causale? Visages de la causalité en sciences sociales et recherche qualitative
Bibliographic record
Abstract
Cet article présente la face cachée du langage sur la causalité en sciences sociales et montre que nous faisons des "analyses causales" même lorsque nous n'en sommes pas conscients à première vue. En outre, il attire l'attention sur une nouvelle représentation de la pensée causale qui met en valeur la recherche des "pouvoirs causals " des relations sociales et sur le fait que la recherche qualitative contribue à recentrer l'analyse causale conventionnelle. Cette nouvelle conception se situe alors, paradoxalement, à l'intersection de philosophies qui se présentent comme opposées, en particulier le " réalisme " et le " constructivisme ". Chemin faisant, on voit comment les différents types d'énoncés causals sont des formes de construction de sens, ce qui nous amène à reconnaître, entre autres choses, IVincomplétude" de toute analyse causale et le rôle "créateur de sens" du cadre théorique privilégié.
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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 | 0.048 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".