Impact of COVID-19 on the Employment of Immigrants
Bibliographic record
Abstract
The Canadian economy has been suffering from the damaging impact of COVID-19. The adverse impact of COVID-19 on employment and income has been unevenly affecting different socio-economic and demographic groups in Canada. Labour market impact of COVID-19 disproportionately affected immigrants, particularly women as they are overrepresented in low paid and precarious work in Canada. Although federal emergency benefits were provided such as Canada Emergency Response Benefit (CERB), marginalized workers were excluded from these benefits as they were not able to meet the eligibility criteria. Based on the interviews of 20 women from the Bangladeshi community in the Greater Toronto Area my research finds that neoliberalism contributes to the rise of precarious employment and labour market insecurity and the COVID-19 pandemic exposed the stark contrast in divisions in the labour market between workers with relatively secure jobs and the ability to work from home, those without the ability to work from home (especially in precarious jobs) and those who lost their jobs due to the pandemic. My findings show that a majority of immigrant Bangladeshi women in the Greater Toronto Area who were employed were working in precarious jobs that were low-paying, temporary or contractual in nature. I find a high level of job loss, due to the COVID-19 pandemic, disproportionately experienced by immigrant Bangladeshi women as they are more vulnerable and marginalized in Canada.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".