Valoriser les données d’enquêtes qualitatives en sciences sociales : le cas français de la banque d’enquête beQuali
Bibliographic record
Abstract
Réutiliser des matériaux d’enquêtes qualitatives en sciences sociales pour produire de nouvelles recherches et enseigner les méthodes : tel est le questionnement scientifique ayant conduit en 2011 à la création de la banque d’enquêtes qualitatives au Centre de données socio-politiques (CDSP). Le présent article expose les réflexions menées au sein de beQuali autour des enjeux de la réutilisation des données qualitatives. Il détaille pour chaque étape du processus — de la collecte des archives à l’exploration des corpus sur le site Web — les différentes problématiques qui ont préfiguré la mise en place du dispositif beQuali et les moyens mis en oeuvre pour construire et faire fonctionner l’équipement tel qu’il existe aujourd’hui.
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 | 0.339 | 0.483 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".