EDITORIAL Introduction: work, learning and transnational migration
Bibliographic record
Abstract
When transnational migrants arrive in a new country, many of them face multifaceted barriers in transition into work and learning in the host society, with language and employment as the most frequently cited barriers. In the context of Canada, for example, despite the fact that immigrants bring significant human capital resources to the Canadian labour force, my research has shown that many highly educated immigrant professionals experienced deskilling and devaluation of their prior learning and work experience upon arrival (Guo 2009, 2013a, 2013b). One troubling aspect of the deskilling experience is the precarious nature of work and learning for immigrants, characterised by part-time employment, low wages, job insecurity, high risk of poor health and limited social benefits and statutory entitlements (Guo 2013a). As a consequence, many have suffered unemployment and underemployment, poor economic performance and downward social mobility. Recent immigrants’ negative experience in Canada can be attributed to a triple glass effect, including a glass gate, glass door and glass ceiling, which may converge to create multiple structural barriers and affect immigrants’ new working lives at different stages of their integration and transition processes (Guo 2013b).
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".