The Youngest Bilingual Canadians: Insights from the 2016 Census Regarding Children Aged 0–9 Years \n
Bibliographic record
Abstract
Dans cette recherche, nous avons utilisé les données du Recensement Canada de 2016 pour examiner le bilinguisme à la maison chez les enfants âgés entre 0 et 9 ans. Les données ont montré que dix-huit pour cent des enfants parlaient au moins deux langues à la maison à l’échelle du Canada. Ce chiffre grimpe à plus de 25% dans les grandes villes et sur les Territoires canadiens. La combinaison français-anglais était la plus répandue au Québec et en Ontario. D’autres combinaisons variées étaient également remarquées dans la plupart des provinces. Dans les Territoires, dix-sept pour cent des enfants parlaient une langue autochtone à côté de l’anglais. Nous discutons des occasions spécifiques pour la revitalisation des langues autochtones et des défis s’y rattachant. La présence d’adultes bilingues à la maison et la génération d’émigrés étaient les prédicteurs principaux du bilinguisme de l’enfant à la maison. Nous terminons sur une discussion des politiques qui encouragent le bilinguisme chez l’enfant comme le fait de soutenir la langue parlée à la maison à l’aide de structures éducatives aux niveaux préscolaire et primaire. De telles politiques doivent être adaptées aux besoins particuliers des communautés pour appuyer de manière optimale les enfants bilingues et leurs familles. Abstract: In this study, we used 2016 Canadian Census data to examine home bilingualism among children aged 0–9 years. Across Canada, 18 percent of children used at least two languages at home, which rose to more than 25 percent in large cities and the Canadian territories. English and French was the most common language pair in Quebec and Ontario, and various other pairs were spoken in most provinces. In the territories, 17 percent of children spoke an Indigenous language and English, and we discuss specific opportunities and challenges for Indigenous language revitalization. The presence of bilingual adults in the home and immigration generation were the strongest predictors of children’s home bilingualism. We conclude by discussing how policies can encourage child bilingualism, such as by supporting children’s home language in early and primary education settings. Such policies must be tailored to the needs of the specific communities to optimally support bilingual children and their families.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".