Preparing for Success in Canada and the United States: the Determinants of Educational Attainment Among the Children of Immigrants
Bibliographic record
Abstract
This paper reviews the recent research on the determinants of the educational attainment among the children of immigrants (the 2nd generation) in Canada and the United States. The focus is on the gap in educational attainment between the 2nd and 3rd-and-higher generations (the children of domestic born parents), as well as the intergenerational transmission of education between immigrants and their children. On average, the children of immigrants have educational levels significantly above their counterparts with domestic born parents in Canada. In the U.S., educational levels are roughly the same between these two groups. In both countries, conditional on the educational attainment of the parents and location of residence, the children of immigrants outperform the 3rd-and-higher generation in educational attainment. Parental education and urban location are major determinants of the gap in educational attainment between the children of immigrants and those of Canadian or American born parents. However, even after accounting for these and other demographic background variables, much of the positive gap between the 2nd and 3rd-and-higher generations remains in Canada. In Canada, parental education is less important as a determinant of educational attainment for the children in immigrant families than among those with Canadian-born parents. Less educated immigrant parents are more likely to see their children attain higher levels of education than are their Canadian-born counterparts. Outcomes vary significantly by ethnic/source region group in both countries. In the U.S., some 2nd generation ethnic/source region groups, such as those with Mexican, Puerto Rican, Central American backgrounds, have relatively low levels of education, even though conditional on background characteristics they outperform their 3rd-and-higher generation counterparts. In contrast, in Canada, children of the larger and increasingly numerically important immigrant groups (the Chinese, South Asians, Africans, etc) register superior educational attainment levels to those of the 3rd-and-higher generation. This result is partly related to the high levels of parental education and group-level “ethnic capital†among these immigrant groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".