Effects of Spatial Characteristics on Non-Standard Employment for Canada’s Immigrant Population
Why this work is in the frame
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Bibliographic record
Abstract
Using microdata from Statistics Canada’s Labour Force Survey (LFS) and Population Census, this paper explores how spatial characteristics are correlated with temporary employment outcomes for Canada’s immigrant population. Results from ordinary least square regression models suggest that census metropolitan areas and census agglomerations (CMAs/CAs) characterized by a high share of racialized immigrants, immigrants in low-income, young, aged immigrants, unemployed immigrants, and immigrants employed in health and service occupations were positively associated with an increase in temporary employment for immigrants. Furthermore, findings from principal component regression models revealed that a combination of spatial characteristics, namely CMAs/CAs characterized by both a high share of unemployed immigrants and immigrants in poverty, had a greater likelihood of immigrants being employed temporarily. The significance of this study lies in the spatial conceptualization of temporary employment for immigrants that could better inform spatially targeted employment policies, especially in the wake of the structural shift in the nature of work brought about by the COVID-19 pandemic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it