Langues, identités et francophonie chez des étudiants universitaires issus de l’immersion française à Vancouver, au Canada
Bibliographic record
Abstract
Abstract: Canada's federal policy of multiculturalism within a bilingual framework (1971) means that in British Columbia French remains a modern language with a certain degree of privilege. While in BC only 1.3% of the population describe their first language as French (Statistics Canada, 2006), French immersion education is growing, with 7.6% of children studying in French immersion schools between kindergarten and grade 12 (BC Ministry of Education, 2010). Many studies have analyzed the learning of French in Canadian French immersion schools (Cummins, 1983; Dagenais & Day, 1999; Dagenais & Moore, 2008; Heller, 2001, 2006; Swain, 1974, 2000). However, there has been a lack of research into the language and literacy practices of French immersion graduates enrolled in anglophone universities. We address this issue by presenting data from an ongoing three-year longitudinal study, funded by the Social Sciences and Humanities Research Council of Canada, into the language and literacy practices of French immersion graduates studying at an English-medium university in Vancouver, Canada. In this context, the study focuses on the complex interaction between language practices, processes of identity construction, and discourses around the French language which the participants in the study encounter in their social and educational spaces.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".