Le développement des méthodes de verbalisation de l’action : un apport certain à la recherche qualitative
Bibliographic record
Abstract
Devant les limites inhérentes de l’observation du comportement, la verbalisation de l’action a connu un essor remarquable au cours des dernières décennies auprès de chercheurs s’intéressant à la dimension privée des activités et des pratiques humaines. Passant du statut de méthode complémentaire, voire de pis-aller méthodologique, à celui de véritable stratégie de recherche, la verbalisation de l’action se reconnait aujourd’hui dans une panoplie de techniques et de méthodes qui endossent les postulats de l’approche qualitative de nature compréhensive. Notre contribution vise à présenter un éventail de techniques et de méthodes de verbalisation de l’action, regroupées selon leur paradigme épistémologique, à faire ressortir la nature des données qu’elles prétendent fournir et à montrer en quoi certains de ces outils méthodologiques, ceux conçus en appui sur les balises de l’approche compréhensive, constituent un apport certain à la recherche qualitative.
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 | 0.160 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".