Canadian Immersion Students’ Investment in French
Bibliographic record
Abstract
Abstract French second language education, including the option of one‐way French immersion, is mandated for majority‐language Anglophone children in New Brunswick, Canada's only officially bilingual province. Language ideological debates in the province surrounding official English–French bilingualism led us to investigate adolescent majority‐language immersion students’ investment in French, the co‐official minority language, using Darvin and Norton's tripartite (capital, ideology, identity) model. We discuss 3 student profiles, drawing on data collected from multimodal focus groups conducted among 8th‐grade French immersion students. Our analysis reveals a dominance of neoliberal ideologies in these students’ investment in French, rendering it imbalanced and largely driven by imagined access to future economic capital. Language as cultural or social capital figures inconsistently in their investment. Drawing on our data, we conclude by proposing that Darvin and Norton's model, with a balanced focus on each kind of capital within the model, may be used conceptually by educators in program development. The model used in such a way would enable educators to give equal priority to students’ identity and intercultural development as to their preparation for participation in economic marketplaces, thus potentially expanding majority‐language students’ investment in their co‐official minority second language.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".