Revue de trois publications portant sur l’analyse secondaire en recherche qualitative
Bibliographic record
Abstract
Peu de livres traitent de l’analyse secondaire en recherche qualitative. Et lorsqu’il est possible d’en trouver, rien n’assure que l’auteur en question possède une conception de l’analyse secondaire qui rejoint la nôtre. En effet, il existe plusieurs types d’analyses secondaires en recherche qualitative et il s’agit d’un des points que nous ressortons de la lecture des trois ouvrages étudiés dans le présent texte. Chacun des livres exposent ainsi une vision de l’analyse secondaire et abordent des thèmes – les processus, les formes, l’éthique, etc. – qui les distinguent et qui, dans une certaine mesure, peuvent les rendre complémentaires. Ce sera au lecteur-chercheur de trouver ici une approche qui lui sied; c’est en ce sens que les courtes présentations contenues dans ce texte se veulent des outils en vue de recherches méthodologiques plus approfondies.
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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 | 0.102 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".