Bibliographic record
Abstract
Cet article propose une synthèse des résultats principaux d’une recherche doctorale portant sur le rapport aux savoirs d’étudiantes françaises musulmanes, d’origine maghrébine, vivant en France, et dont la particularité est de porter le hijâb et de fréquenter régulièrement la mosquée. Dans un premier temps, en retraçant les stratégies identitaires de dix-sept étudiantes moutahajibâte, on a tenté de comprendre comment l’adoption de « la stratégie du retournement du stigmate » par ces jeunes femmes accentue leur rapport conflictuel aux savoirs laïques. Dans un second temps, on a exploré les fonctions psychiques du hijâb permettant de réajuster le « Moi-peau » chez ces jeunes femmes ainsi que leurs conflits internes des savoirs. This article presents a synthesis of a doctoral study relating to French Muslim women students’ relation to knowledge. The participants are of North African origin; they live in France and their custom is to wear the hijâb and to attend the mosque regularly. First of all, the purpose of relating the records of seventeen veiled (moutahajibâte) students’ identity strategies was to try to understand how adopting “the strategy of returning to stigmatization” by these young women increased their conflicting relationship to secular learning. Secondly, the psychic functions of the hijâb that permit the women to readjust the constancy of their self-identity of the young women as well as their internal learning conflicts was explored.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".