Immigration, Citizenship and Racialization at Work: Unpacking Employment Precarity in Southwestern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the relationship between precarious employment, legal status, and racialization. We conceptualize legal status to include the intersections of immigration and citizenship. Using the PEPSO survey data we operationalize three categories of legal status: Canadian born, foreign-born citizens, and foreign-born non-citizens. First we examine whether the character of precarious work varies depending on legal status, and find that it does: Citizenship by birth or naturalization reduces employment precarity across most dimensions and indicators. Next, we ask how legal status intersects with racialization to shape precarious employment. We find that employment precarity is disproportionately high for racialized non-citizens. Becoming a citizen mitigates employment precarity. Time in Canada also reduces precarity, but not for non-citizens. Foreign birth and citizenship acquisition intersect with racialization unevenly: Canadian born racialized groups exhibit higher employment precarity than racialized foreign-born citizens. Our analysis underscores the importance of including legal status in intersectional analyses of social inequality.
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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 | 0.001 | 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 | 0.001 | 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