The Effects of Educational-Occupational Mismatch on Immigrant Earnings in Australia, with International Comparisons
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the way immigrant earnings are determined in Australia. It uses the overeducation/required education/undereducation framework and a decomposition of the native-born/foreign-born differential in the payoff to schooling. This decomposition links over-education to the less-than-perfect international transferability of immigrants’ human capital, and undereducation to favorable selection in immigration. Comparisons are offered with findings from analyses for the U.S. and Canada to enable assessment of the relative impacts of favorable selection and the limited international transferability of human capital to the lower payoff to schooling for the foreign born. The sensitivity of the results of the decomposition to several measurement issues is assessed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it