The role of plurilingual parenting in parental engagement of immigrant families
Bibliographic record
Abstract
Due to the increased mobility and linguistic and cultural diversity internationally, there has been a renewed interest in the linguistic practices of immigrant families. Earlier scholarship focused on the difference between parenting in monolingual contexts and bilingual parenting conceptualised as management of more than one language in a family. To better understand the complexity of language practices in immigrant families, this article develops a new concept of plurilingual parenting. This analysis is based on empirical data from Canada and uses plurilingualism as a theoretical framework. I found that immigrant parents adopt plurilingual parenting, which is characterised by the following features: (1) parental beliefs in the dynamic and fluid nature of language practices; (2) family language policies that are flexible and allow for partial proficiency in languages in familial linguistic repertoires; and (3) interconnectedness of language and culture. Implications include the possibility to use the concept of plurilingual parenting in the scholarship related to family language policy and identity negotiation in immigrant families. Educators working with immigrant students will benefit from the familiarity with the concept of plurilingual parenting by aligning their expectations with parental practices and appreciating students’ funds of knowledge.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".