Employment integration experiences of Syrian refugee women arriving through Canada’s varied refugee protection programmes
Bibliographic record
Abstract
This article examines the employment integration experiences of Syrian refugee women arriving to Canada through four common refugee streams: government-assisted, private sponsorship, Blended Visa office referral, and as refugee claimants. Drawing on in-depth, semi-structured interviews with Syrian refugee women and key informants, we show how differences between streams – eligibility requirements, supports provided, services rendered – facilitate or act as barriers to seeking out and securing employment. The finding suggests that government-assisted refugee women struggled the most when compared with the other refugee women. Particularly, GAR women arrived with part of their families and minimal supports, affecting their mental well-being and job search. Meanwhile, the other refugee women typically arrived with the qualities (language, work experience) needed to integrate and so, were able to choose when, how, and the type of work they secured. Through this study, we propose policy recommendations that should be incorporated within the Canadian refugee system to mediate challenges and promote a positive resettlement experience for all refugees.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".