Human Capital and Earnings of Female Immigrants to Australia, Canada, and the United States
Bibliographic record
Abstract
Census data for 1990/91 indicate that Australian and Canadian female immigrants have higher levels of English fluency, education (relative to native-born women), and income (relative to native-born women) than do U.S. female immigrants. A prominent explanation for this skill deficit of U.S. immigrant women is that the United States receives a much larger share of immigrants from Latin America than do the other two countries. Similar to previous findings for male immigrants, the apparent skill disadvantage of foreign-born women in the\nUnited States (relative to foreign-born women in Australia and Canada) shrinks dramatically once we exclude immigrants originating in Latin America. In all three countries, men are much more likely than women to gain admission on the basis of immigration criteria related to labor market considerations rather than family relationships. For this reason, we might expect that the stronger emphasis on skill-based admissions in Australia and Canada compared to\nthe United States would have a larger impact on cross-country differences in the skill content of male rather than female immigration flows. Therefore, our findings of similar patterns for men and women and of the key role played by national origin both suggest that factors other than immigration policy per se are important contributors to the observed skill differences between immigrants to these three destination countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".