Racial Variations in Ethnic Identity among the Children of Immigrants in Canada
Bibliographic record
Abstract
Using data from the Ethnic Diversity Survey, we study the impact of parental human capital, family socialization, co-ethnic friendship, and experience of discrimination on ethnic identity formation among the children of immigrants from diverse ethnic backgrounds. In addition, we examine the extent to which these factors account for racial variations in ethnic identity attainment. The results show that racial minority children of immigrants are more likely to attain ethnic identity than their white counterparts. We also find racial variation is explained by parental human capital, family socialization, co-ethnic friendship, and past experience of discrimination. We discuss implications of our findings for Canada’s multiculturalism policy, which promotes the formation and maintenance of ethnic identity. Dans cette étude, nous utilisons les données de l’Enquête sur la diversité ethnique pour examiner l’impact du capital humain des parents, la socialisation familiale, les rapports entre amis de différentes ethnies, et l’expérience de la discrimination sur la formation de l’identité ethnique des enfants d’immigrants de diverses origines ethniques. Nous cherchons aussi à voir si ces facteurs expliquent les variations raciales dans la construction de l’identité ethnique. Les résultats montrent que les enfants d’immigrants de minorité raciale sont plus susceptibles de se forger une identité ethnique que leurs homologues blancs. De plus, les quatre facteurs expliquent la variation raciale. Nous discutons des implications de nos résultats pour la politique canadienne du multiculturalisme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".