Acquisition and Employment Consequences among Southeast Asian Refugees in Canada
Bibliographic record
Abstract
Lorsqu’elles sont arrivées au Canada, les femmes réfugiées provenant du sud-est de l’Asie avaient moins de chance de parler l’anglais que les hommes en avaient. L’avantage linguistique des hommes était encore en évidence une décennie plus tard. Les femmes avaient moins d’opportunités que les hommes d’apprendre l’anglais durant la période suivant la migration. Par contre, avec ironie, les femmes ont bénéficié encore plus que les hommes d’opportunités comme des cours de langue seconde en anglais. Cette abilité pour parler l’anglais permet d’augmenter les chances de demeurer dans le marché du travail. Cet effet était encore plus fort chez les femmes que chez les hommes. Les politiques de transfert de population doivent assurer une opportunité sans biais d’acquérir la langue de la société qui reçoit. When they arrived in Canada, female Southeast Asian refugees were far less likely than males to speak English. The male linguistic advantage was still in evidence a decade later. Women had fewer opportunities than men to learn English during the post-migration period. Ironically, however, women benefited even more than their male counterparts from opportunities such as English as a second language (ESL) classes. English-language ability improved the likelihood of staying in the labour market. This effect was even stronger for women than for men. Resettlement policies must ensure unbiased opportunity to acquire the language of the receiving society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".