Le concept d’emergent-fit dans les approches méthodologiques inductives
Bibliographic record
Abstract
Cet article aborde l’un des aspects méthodologiques centraux des approches inductives, celui de l’emergent-fit, qui est aussi l’une des propriétés principales de la recherche qualitative. Ce concept est souvent associé à l’analyse et, conséquemment, à la collecte des données au fur et à mesure de l’analyse et de l’échantillonnage théorique qui se précise. Pour la réalisation de la recherche qui est à l’origine de cet article, nous avons analysé les données qui proviennent des traces méthodologiques de nos différents projets de recherche : journal réflexif de recherche, mémos, recension d’écrits scientifiques. Nos résultats montrent que l’emergent-fitconcerne l’analyse des données, mais aussi d’autres principes de la recherche, dont la problématique provisoire qui évolue, la sensibilité théorique, l’échantillonnage théorique, la flexibilité méthodologique, l’intégration théorique ou encore la saturation théorique. L’emergent-fitest un processus qui se caractérise par de la transversalité et qui a sa propre dynamique.
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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 | 0.093 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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; 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".