Explaining the Deprofessionalized Filipino: Why Filipino Immigrants Get Low-Paying Jobs in Toronto
Bibliographic record
Abstract
Processes of labour market subordination among Filipino immigrants to Canada have been widely observed in recent years, but the reasons for them have usually been assumed to be typical of all immigrant groups. While some processes behind deprofessionalization and mismatched skills in the labour market are indeed generic and experienced by all immigrants arriving with non-Canadian credentials and experience, particular groups experience the labour market in specific ways. In this paper, we seek to provide a nuanced assessment of the factors behind the deprofessionalization of Filipino immigrants in particular, by drawing attention to a mixture of cultural, economic, social and institutional circumstances that shape the experience of this group. We argue that the distinctive labour market integration processes affecting Filipino immigrants requires attention by policy makers, and by implication we also suggest the importance of considering the distinctive labour market experiences of other specific groups. The generic immigrant experience that so often forms the basis of quantitative or institutional assessments of labour market integration should not be assumed to be universally applicable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".