Asian immigrants' integration into the Australian labour market: How their skills are being utilized
Bibliographic record
Abstract
The Australian economy is facing an acute skill shortage, and the country’s dependence on skilled immigrants has significantly increased. Australia has a well-devised skilled immigration policy. Asians constituted 41% of immigrants in 2007. Generally, immigrants are categorised into non-English Speaking Background (NESB) and English-Speaking Background (ESB). It is argued that after arriving in Australia, NESB migrants face more labour market disadvantages than ESB immigrants. This paper investigates the labour market disadvantages of Asian immigrants (those whose language spoken at home is Chinese, Vietnamese, Southeast, Northeast or Southern Asian, as defined by the Department of Immigration and Citizenship). This paper argues that Asian immigrants face various forms of unemployment, underpayment, deskilling and discrimination in the Australian labour market. This paper also attempts to examine the issues relating to labour market integration confronted by Asian immigrants in Canada and New Zealand. Recent studies reveal that language is the main barrier for Asian immigrants to integration into the labour market of a host country. Non-recognition of qualifications also causes difficulties. Acculturation of Asian immigrants in a new environment often becomes challenging due to a ‘big gap’ between them and the natives. Research findings also demonstrate that Asian immigrants face employment discrimination in various forms, sometimes simply because they are Asian.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".