Défis d’insertion professionnelle spécifiques à l’acculturation d’enseignant⋅e⋅s immigrant⋅e⋅s au Québec
Bibliographic record
Abstract
Les enseignant⋅e⋅s immigrant⋅e⋅s sont une richesse pour les sociétés d’accueil en raison de leur biculturalité (Niyubahwe et coll., 2019). Pourtant, ces membres de la profession enseignante rencontrent nombre d’obstacles qui peuvent compromettre leur projet migratoire (Duchesne, 2017). L’article porte sur les difficultés d’acculturation en période d’insertion professionnelle au Québec. L’analyse est soutenue par les concepts de parcours migratoire (Legault et Fronteau, 2008), d’acculturation (Berry, 2005) et d’insertion professionnelle en enseignement (Mukamurera et coll., 2013). La discussion mène à une adaptation du modèle d’insertion professionnelle tenant compte de la phase postmigratoire et à des pistes de réflexion liées aux besoins des enseignant⋅e⋅s immigrant⋅e⋅s.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".