Educational Attainments of Immigrant Offspring: Success or Segmented Assimilation? <sup/>
Bibliographic record
Abstract
In this article, I study the educational attainments of the adult offspring of immigrants, analyzing data from the 1996 panel of the Survey of Labour and Income Dynamics (SLID). Fielded annually since 1993 by Statistics Canada, respondents are asked for the first time in 1996 to report the birthplaces of their parents, making it possible to define and study not only the foreign-born population (the first generation), but also the second generation (Canadian born to foreign-born parents) and the third-plus generation (Canadian born to Canadian-born parents). The survey also asked respondents to indicate if they are members of a visible minority group, thus permitting a limited assessment of whether or not color conditions educational achievements of immigrant offspring. I find that “1.5” and second generation adults, age 20–64 have more years of schooling and higher percentages completing high school compared with the third-plus generation. Contrary to the segmented “underclass” assimilation model found in the United States, adult visible minority immigrant offspring in Canada exceed the educational attainments of other not-visible-minority groups. Although the analysis is hampered by small sample numbers, the results point to country differences in historical and contemporary race relations, and call for additional national and cross-national research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".