Language attitudes towards French: A mixed-method investigation on potential Chinese immigrants in Ontario and Quebec Canada
Bibliographic record
Abstract
This mixed-methods study aims to explore the language perceptions of Chinese individuals who live in Canada and consider immigration, specifically focusing on their attitudes towards French. The goal is to understand their attitudes towards French and, ultimately, increase policy makers’ awareness of the future maintenance of French in Canada and re-evaluating the current language teaching approach. Seventy-eight Chinese participants from Quebec or Ontario regions completed two questionnaires that were derived from the Belief about Ethnolinguistic Vitality framework, followed by a semi-structured interview conducted with sub-sampled nine participants to explore reasons behind their attitudes. In terms of analyses and results, although Quebec participants believed that French would become more commonly valued and used in the future, both Ontario and Quebec participants claimed that French would not be as essential as English, and they had more positive attitudes towards the English acquisition because of its great regional power and instrumental benefits. Additionally, a multi-regression analysis demonstrated that attitudes towards French engagement were affected by sociocultural differences and language proficiency but were not affected by educational contact (French language course). The findings of thematic analysis indicate that problems about the monolingual teaching approach led to a negative view of French lessons and even a negative attitude towards engaging with the French language. Finally, practical implications and suggestions were provided in order to enhance their attitudes towards French engagement in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".