Marginalisation des cultures éducatives des francophones Autres en contexte francophone minoritaire
Bibliographic record
Abstract
Ce présent article poursuit la publication des résultats de notre recherche doctorale (Prophète, 2020) sur les enjeux identitaires et l’insertion professionnelle des nouveaux enseignants issus des communautés francophones immigrantes en contexte francophone minoritaire, faisant suite au texte (Prophète, 2022). Il adresse particulièrement les enjeux qui interviennent dans l’intégration identitaire des francophones immigrants non-européens dans la société d’accueil, notamment dans le milieu professionnel hôte. La méthodologie est qualitative et interprétative. L’approche compréhensive (l’entretien compréhensif et le récit de vie) qui est la nôtre permet d’accéder à l’expérience migratoire et professionnelle de nos participants. L’analyse des discours axés sur le contenu permet de mettre à jour les défis d’adaptation et d’intégration qui marginalisent ces derniers dans les communautés éducatives hôtes. L’analyse révèle également les stratégies qu’ils déploient pour combler les écarts entre leurs propres représentations culturelles éducatives et celles du milieu hôte.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".