ARE ASIAN MIGRANTS DISCRIMINATED AGAINST IN THE LABOR MARKET? A CASE STUDY OF AUSTRALIA
Bibliographic record
Abstract
This paper explores the issue of discrimination against Asian migrants relative to their non-Asian counterparts in the Australian labour market. A unique and consistent data set from three waves of the Longitudinal Survey of Immigrants to Australia (LSIA, 1993–95) is used to estimate probit models of the probability of being unemployed separately for males and females of Asian and non-Asian origins. The unemployment probability gap between the two migrant groups is decomposed into two components, the first associated with differences in their human capital and other demographic characteristics, and the second with differences in their impacts (called discrimination). The results provide an evidence of discrimination against Asian male migrants in all three waves. Discrimination against Asian females is detected only in the first wave. The Asian females who are professionals and can speak English 'well' are rather favoured relative to their non-Asian counterparts. Thus, the empirical evidence on discrimination against migrants of Asian origin is mixed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".