The ‘survival job’ pathway: Risk-focusing and occupational challenges among Canadian racialized and immigrant adults during the COVID-19 pandemic
Bibliographic record
Abstract
The impacts of the COVID-19 pandemic were not equally distributed, with emerging research demonstrating that racialized and immigrant populations in Canada have been disproportionately affected. Drawing on the risk-focusing hypothesis, a theoretical lens that explicates how risk burden concentrates disproportionately in marginalized populations, government mitigation strategies such as shutdowns can be seen as exacerbating existing social and economic challenges, making individuals more susceptible to COVID-19 and its adverse outcomes. This paper examines the economic and occupational challenges experienced by individuals from racialized and/or immigrant populations in the Peel Region (Ontario, Canada) during COVID-19 shutdowns. Semi-structured interviews (n = 46) were conducted from October to December 2021, discussing participants' experiences throughout the pandemic. Using thematic analysis, interviews were coded for concepts related to employment, economics, and government support. Semantic codes were grouped into categories, where themes were identified and refined. Loss of work and difficulties in securing employment were primary themes across interviews, with participants discussing taking on low-skill "survival jobs" to pay the bills. Participants spoke of being worried about finances and decreased mental health as a result. An additional theme was the pathways of risk that accompanied employment. While government economic and social programs were considered helpful, challenges were noted in navigating resources. This study highlights the importance of examining pandemic outcomes from a risk-focusing framework, to understand how certain groups were repeatedly put at risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".