L’utilisation combinée du récit et du calendrier de vie dans un dispositif d’enquête narrative biographique
Bibliographic record
Abstract
L’objectif de cet article est d’analyser différentes applications d’un dispositif d’enquête narrative biographique à partir de trois enquêtes qualitatives portant sur la transition vers l’âge adulte, les parcours de participation sociale des jeunes et les trajectoires professionnelles de professeures d’université. Dans un premier temps, nous expliquons comment l’utilisation du calendrier de vie, en combinaison avec un entretien narratif, permet de consolider la construction et l’analyse de données relatives aux temps subjectif et objectif. Dans un deuxième temps, nous analysons comment le calendrier devient un outil d’élicitation qui permet de coconstruire des données biographiques et de rendre compte de la réflexivité des personnes enquêtées. Finalement, nous discutons de l’apport de cette approche dans une perspective d’ontologie réaliste critique.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".