Minimising skills wastage: Maximising the health of skilled migrant groups
Bibliographic record
Abstract
Skilled migration is a key element in Australias strategy to address major humancapital issues and imperatives, however underutilisation and atrophy of professionalmigrant skills remains a critical problem. The proposed project aimed to identify barriersand innovative strategies for ensuring utilisation of professional migrants skills andto investigate the links between workforce participation and health. The researchused a sequential exploratory mixed methods design comprising both qualitative andquantitative data collection methods across three research phases. Phase 1: Examined literature on skilled migration in Australia, Canada and NewZealand. An analysis of Census data and data from the Department of Immigration andBorder Protection was also undertaken. Phase 2: Thirteen semi-structured interviews were conducted with key stakeholdersworking in government, policy, industry representation and community based services.The findings from phase 2 of the study informed the development of the quantitativeonline survey. Phase 3:508 skilled migrants responded to an online survey and 14 were theninterviewed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".