TWO STEPS BACK, THREE STEPS FORWARD: THE STORY OF SOUTH AFRICAN MIGRANTS WORKING IN AUSTRALIA
Bibliographic record
Abstract
Skilled migration has become a targeted and intentional instrument by a number of countries in an attempt to ensure skill sufficiency and as a driver of continued economic growth and development. Although countries like the United States of America, Canada, New Zealand and Australia have been able to attract significant numbers of skilled migrants through a combination of pull factors, not the least of which government regulations, this has not always translated into the optimal recognition and use of the human capital of the migrants. Since some literature argues a separation between social and economic integration, special attention to the integration of migrants into the workforce is justified. In order to assess the integration though it is crucial to understand the demographics, experience and phases that migrants go through when settling in a new work environment. This paper reports the narrative of South African migrants establishing themselves in the Australian workforce. It determines that this group of migrants seems to predominantly experience aspects of integration in the labour force as reported in other literature, confirming the validity of earlier research while providing a picture of individual and specific challenges and experiences.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".