The migration of social workers to and from the United Kingdom: a comparative perspective
Bibliographic record
Abstract
This article reports findings from a large mixed-method study exploring the migration to the United Kingdom (UK) of social workers trained in Australia, Canada, India, Romania, South Africa, the US, and Zimbabwe, and the migration of British trained social workers to Australia. The project aimed at exploring the motivations for migration, the experiences of integration, and the impact of culture on these. This article focuses on the quantitative findings and will use some of the qualitative data to further explain and interrogate the differences between these groups based on their country of origin. The findings show the greater challenges migrants from developing countries have experienced, including lack of recognition of their qualifications and experience, and discrimination. The findings also show that contrary to common assumptions, the migration experiences of social workers coming from Australia, Canada and the US are not as easy as expected. American social workers who migrated to the UK turned out to be the group least professionally satisfied. The British in Australia on the other hand, were the most satisfied. Implications for practice and future research are explored.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".