Bibliographic record
Abstract
Analyse causale et récits de vie Un des objectifs de cette étude est de contribuer au développement de la réflexion théorique sur l'analyse causale dans les recherches qualitatives et. par ricochet, à l'élucidation de la problématique de la causalité en sciences sociales. L'auteur restitue à l'expression " analyse causale " l'acception large que lui avait donnée Weber. La réflexion méthodologique a comme point d'ancrage deux recherches avec des récits de vie dans le domaine de la sociologie du droit pénal. L'auteur traite du rôle des questions paradigmatiques dans l'analyse causale et indique deux cheminements possibles pour l'imputation causale. Il propose la distinction entre les effets de premier et de deuxième ordre et soulève la question du statut théorico-méthodologique des effets dans les recherches qualitatives. Enfin, il offre une solution structurale à la logique de la causalité.
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 | 0.033 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".