L’adaptation à la diversité ethnoculturelle : quels enjeux pour l’école franco-albertaine ?
Bibliographic record
Abstract
L'ecole francophone en Alberta connait depuis quelques annees une diversification croissante du profil ethnoculturel de ses eleves ayant-droits sous l’effet de la migration internationale. Elle accueille dorenavant des eleves aux profils linguistique, culturel, racial, religieux diversement ancres. En effet, le portrait ethno-demographique realise par le Conseil Scolaire Centre Nord dans six de ses ecoles (CSCN, 2003) revele que le pourcentage d’eleves immigrants frole les 50% et qu’ils sont originaires de 23 pays differents, dont majoritairement des pays de l’Afrique Subsaharienne. Dans certaines ecoles aujourd’hui, plus de 85% des eleves sont issus de la migration internationale ; la presence d’eleves originaires de l’Afrique subsaharienne y est egalement tres marquee. Si la visee inclusive est au cœur du discours educatif (Alberta Ministry of Education, 2015 ; CSCN, 2017), sur le terrain les acteurs scolaires signalent plusieurs defis. Cette communication presente les donnees preliminaires d’une recherche qualitative realisee dans quatre ecoles secondaires franco-albertaines. Les entrevues individuelles et les groupes de discussion menees avec differents acteurs scolaires, dont les directions d’ecole, les enseignants et les travailleurs en etablissement, permettent d’eclairer les defis rencontres par l’institution scolaire pour repondre aux besoins des eleves issus de la migration internationale et les reponses qu’elle y apporte.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".