Le discours des enseignants d'immersion française en Colombie-Britannique sur l'intégration des perspectives autochtones dans leur pratique
Bibliographic record
Abstract
Integrating Indigenous perspectives in British Columbia is a requirement for teacher education programs as well as in the K-12 school programs. This doctoral research aims to study the successes and challenges of integrating Indigenous perspectives specifically for French immersion teachers in the K-12 programs. Very little research has focused on the integration of Indigenous perspectives in French immersion programs in Canada. In British Columbia, in particular, there has been no research on this topic. In this qualitative multiple case-study research, semi-structured interviews were conducted with six French immersion teachers. The theoretical framework informing this research is based on the following field of studies : White supremacy and privilege studies in the context of settler colonialism; antiracist education; curriculum studies and decolonization. For presenting this research, we chose a manuscript-based format thesis that includes three publications (Côté, 2019a, Côté, 2019b, Côté, submitted). First, the lack of research done in French is explored on two distinct levels: (1) decolonization, settler colonialism and White supremacy, and (2) the integration of Indigenous perspectives in the preservice teacher education program as well as in the K-12 school programs. Second, the similar challenges encountered by both French and English teachers are explored. Also, two challenges unique to the French immersion program are briefly presented.
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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 | 0.003 | 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 | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".