Intergenerational language transmission in Quebec: patterns and predictors in the light of provincial language planning
Bibliographic record
Abstract
The study presented here is the first empirical investigation of the patterns and predictors of the intergenerational transmission of French in Quebec. An online questionnaire was used to gather data from 274 parents from different mother tongue (L1) groups: L1 French, L1 English, L1 French and English, and L1 Other. The results show that L1 French-and-English-speaking parents and L1 Other parents displayed particularly low rates of French transmission. Three variables were found to be significant predictors of the intergenerational transmission of French: having it as one’s L1, high proficiency, and positive attitudes towards the language on the solidarity dimension. The same three variables were also found to be significant predictors for the intergenerational transmission of English in Quebec, indicating that they may not be merely language-specific. Not significant for either French or English were language used with partner, attitudes on the status dimension, Quebec-based social identity, migration background, and location within Quebec. Further research is needed to ascertain whether the identified predictors are context-specific, and what other variables are at play. The article discusses the implications of this study’s findings for theory as well as for language planning in Quebec, and especially the potential effectiveness of acquisition and prestige planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".