Stratégies d’encadrement favorisant l’insertion professionnelle des nouveaux enseignants issus de l’immigration : l’apport du leadership
Bibliographic record
Abstract
Puisque le Canada accueille de nombreux immigrants chaque année, l’embauche de nouveaux enseignants issus de l’immigration est une pratique de plus en plus courante au pays. Les études portant sur la transition à l’emploi vécue par les enseignants francophones issus de l’immigration sont peu nombreuses. C’est pour cette raison qu’il s’avère important de s’interroger à propos des caractéristiques du leadership à adopter ainsi que des stratégies d’encadrement à préconiser par les directions d’école afin de permettre aux nouveaux enseignants issus de l’immigration de vivre une insertion réussie au sein de la profession. Cet article a donc pour but de présenter les résultats d’une recherche qualitative qui a permis d’examiner le Cadre de leadership de l’Ontario préconisant les trois « C » (Leadership Competencies, Character and Commitment) au regard de l’expérience vécue par six directions d’école ayant supervisé du nouveau personnel enseignant issu de l’immigration.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".