‘When they speak English, it's normal’: the monolingual realities of multilingualism
Bibliographic record
Abstract
Canada is described as a mosaic of many ethnic groups, languages, and cultures, yet its ‘multiculturalism within a bilingual framework’ (Haque, Citation2012. Multiculturalism within a bilingual framework: Language, race, and belonging in Canada. University of Toronto Press) only supports two official languages (French and English). This study of language and identity scrutinises the implications of national language policies on intergenerational language shifts, family language policies, and identity negotiations of second-generation Chinese Canadian youth. Using data generated from a one-year qualitative study, this framework engages conceptualizations of performativity (Butler, Citation2021. Excitable speech: A politics of the performative. 1st ed. Routledge) and emotionality (Ahmed, Citation2014. The cultural politics of emotion. Edinburgh University Press) to analyse parent and student narratives about home language practices. The resulting analysis suggests that a force of power underlies the way speech acts, and emotion support family language policies. Namely, the data demonstrates that intentional and unintentional injurious language uphold the goals of home language maintenance and inscribe cycles of individual and institutional acts of symbolic violence within families. This study argues that family language policies of multilingual families are impacted by the ways cultural diversity is managed by a bilingual nation-state.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.042 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".