Expériences inductives et recherche qualitative collective
Bibliographic record
Abstract
Dans l’optique de contribuer au dépassement des clivages disciplinaires, notamment dans le domaine de la recherche scientifique, cet article vise à mettre en lumière les particularités de la recherche qualitative collective. Pour ce faire, à partir de leur vécu expérientiel dans deux équipes de recherche où la complicité et la mutualisation des ressources et des pratiques existaient, les auteurs interrogent la recherche qualitative dans un cadre collectif où plusieurs chercheurs collaborent, notamment dans l’analyse des données. Pour approfondir leur compréhension de cette pratique collective, les auteurs appuient leurs analyses par le recours à des publications sur le sujet. Il a résulté de ce travail que la recherche qualitative en équipe favorise l’ouverture, la richesse des résultats et l’engagement des chercheurs. Cette situation contribue aussi à la triangulation dans l’analyse.
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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 | 0.071 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".