Bibliographic record
Abstract
Dans cet article, j’étudie des auto-identifications religieuses de femmes musulmanes en Finlande et au Québec, Canada. Je m’appuie sur ma recherche doctorale et sur un corpus de 15 entrevues effectuées en milieu universitaire et j’analyse les identités musulmanes dans le cadre méthodologique et théorique de la Théorie du Soi Dialogique (TSD) à partir d’une conception d’un sujet hétérogène multivocal. Ce cadre me permet de cerner l’identité comme négociée et instable qui est, en même temps, à la recherche d’une certaine cohérence. Dans l’analyse, je souligne les manières de solidifier et de liquéfier l’identité religieuse musulmane dans le discours des participantes. Le but précis est ainsi d’examiner diverses manières dont les participantes se positionnent, et positionnent les autres, dans le processus d’identification et de négociation identitaire musulmane.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".