Family language policy retention across generations: childhood language policies, multilingualism experiences, and future language policies in multilingual emerging Canadian adults
Bibliographic record
Abstract
Introduction: Language policies in multilingual families refer to parents' decisions, whether explicitly articulated or not, regarding which languages will be used in which contexts. However, because most studies that explore language allocation focus on families with young children, they do not address how family language policies impact the retention of a home language through to the next generation. The present study investigates an important perspective on this issue, specifically how emerging adults' childhood experiences with their family language policy relate to the languages they currently use and plan to retain in the future. Methods: In all, 62 multilingual Canadian adults, aged between 17 and 29 years, participated in focus group interviews concerning their experience of language policies in their birth families, their current beliefs concerning language allocation and retention, and their plans about language policy in their future families. Results: The data revealed that not only are most participants interested in retaining their home language, thereby continuing to speak the language in their future families, but most are also open to incorporating additional languages into their policies. Discussion: The results provide insight into how to identify effective heritage language retention policies that transcend generations.
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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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".