The Educational Attainments of the “Second Generation”: A Comparative Study of Britain, Canada, and the United States
Bibliographic record
Abstract
Background This analysis compares the educational attainments of the “new” second generation in Britain, Canada, and the United States using three nationally representative datasets. Objective To assess how the second generation has fared within Western educational systems. The study examines the achievements of seven minority ethnic groups: Africans, Caribbeans, Chinese, Filipinos, Indians, Irish, and Pakistanis. Setting Britain, Canada, and the United States. Research Design Secondary data analysis Conclusions The study suggests that there is a strong association between the educational level of the parental generation and that of the second generation. There is substantial inter-generational progress (measured relative to the majority population in the country of destination), especially among women. Most groups perform as well as or better than members of the majority population of the same age and similar parental background. Chinese of both sexes are notable for their high performance. Indians also tend to make strong intergenerational progress; for Caribbeans, Africans, and Filipinos, this is more muted. The performance of the second generation in Britain is slightly poorer than that in the other countries. This is probably explained by the lower selectivity of the first generation in Britain rather than by institutional features.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".