How do coethnic communities matter for educational attainment? A comparative analysis of the United States and Canada
Bibliographic record
Abstract
The United States and Canada represent two of the largest immigrant-receiving countries. Although both countries have long histories of receiving immigrants, they are viewed differently in their abilities to integrate immigrants and their children. A popular and reoccurring narrative is Canada’s greater ability to integrate immigrants and their children compared with the United States. One possible explanation is that coethnic communities in Canada are more visible and supported by government funding than coethnic communities in the United States, which may differentially affect the outcomes of immigrants’ children in the two countries. Using nationally representative data from the Sensitive General Social Survey and Ethnic Diversity Survey, this study examines the effects of coethnic community, national origin group, and individual characteristics on educational attainment in the United States and Canada. This study utilizes differences in coethnic community and national origin group effects to understand institutional differences between the two countries. In particular, it finds that coethnic community education has a positive effect in both countries, but the effects for coethnic community income and educational selectivity differ. This study suggests that differences in coethnic community income and educational selectivity may be due to differences in immigration policy, which shape the types of settlement challenges and sources of support that immigrants and their children encounter upon arrival.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".