Well Enough to Work? Examining the Mental Health Outcomes of Precarious and Non- Status Migrants who are Precariously Employed in Canada
Bibliographic record
Abstract
Migrants living in precarity face many barriers when navigating Canada’s complex immigration system, with many losing their status through the process. Scholars have identified poor mental health outcomes of precarious and non-status migrants in Canada, but studies are scant and focus largely on fears of deportation, lack of access to health services, social exclusion, and detention. Missing from the research is how precarious employment, which precarious and non-status migrants are overrepresented in, also effects the wellbeing of these individuals. To address this gap in research, this paper explores both media coverage of this population and city council minutes from across Canada. A comparison of both reveals that there is rarely a focus on the mental health outcomes of precarious and non-status migrants, and even less discussions on employment conditions as a contributing factor. Future research is necessary to develop a better understanding of the mental health issues faced by precarious and non-status migrants. Moreover, the narrative of precarious and non-status migrants in the media and during city council meetings must focus on these individuals’ mental health if we are to improve their conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".