Bibliographic record
Abstract
The four chapters that comprise Part Two each draw on research conducted to examine, in turn, the experiences of transnational social workers in Canada, England, Australia, and Aotearoa New Zealand. While the different studies varied in their designs, the numbers of participants involved, and the methods of analysis used to interpret the data, the stories told in each chapter reveal some fundamental consistencies across the four countries. Transnational social workers have been, and continue to be, drawn from across the world to fill social work positions in these jurisdictions. This is the ‘opportunities’ side of the equation. Each of the chapters alludes to the fact that governments in the four countries have aligned their immigration and labour market policies to capitalise on the increased transnational mobility of professional labour, to make it easier for employers to attract transnational professionals and for the transnationals themselves migrate into these nations. It is noteworthy that this has produced a certain degree of ‘churn’ amongst social workers across the four jurisdictions: each of the four national studies includes transnational social workers from the other three countries, in addition to those from many other countries as well. In Chapter Four, Marion Brown, Annie Pullen Sansfaçon, Stephanie Éthier and Amy Fulton describe the ‘pull’ factor to Canada as ‘a discourse of possibility and opportunity’: this is a discourse that is actively mobilised in each of the four countries under discussion in the four chapters in this Part. The equation has another side however: as well as the possibilities and opportunities offered to transnational social workers, there are also challenges. The four chapters share many of these as well: there tends to be an enduring disjuncture between immigration regimes that recognise and credit prospective migrants with foreign social work qualifications and practice experience, and professional regimes that are often less prepared to do so. That disjuncture is often manifest as an open door into the country, and (for some more than for others) a closed door – or at least a stubborn one – into the social work profession. Just as is the case with economic currency, there is a kind of variable exchange rate that impacts on the cultural capital of transnational professionals: receiving countries may value the qualifications and professional experience, as well as language proficiencies, from some jurisdictions more highly than from others.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.082 | 0.020 |
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".