Racialized and gendered disparities in occupational exposures among Chinese and white workers in Toronto
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
OBJECTIVE: We examined disparities in hazardous employment characteristics and working conditions among Chinese and white workers in Toronto, Canada. DESIGN: We used self-administered questionnaire data from a 2005-2006 population-based survey (n = 1611). Using modified Poisson regression, we examined the likelihood for Chinese workers of experiencing adverse exposures compared to whites. Models were stratified by sex and adjusted for differences in human capital. Work sector was conceptualized as a mediating variable. RESULTS: Chinese workers were generally more likely to report adverse exposures. In many cases, disparities were only evident or more pronounced among women. The shorter length of time in Canada of Chinese relative to whites accounted for some of the observed disparities. Meanwhile, the higher educational level of Chinese compared to whites provided them with no protection from adverse exposures. The risk of experiencing discrimination on the labor market and at work was more than 50% higher among Chinese men and women as compared to whites, and those disparities, though reduced, persisted after adjustment for confounders. CONCLUSIONS: Discrimination is far more prevalent among Chinese than among whites and may explain their disproportionate exposure to other hazards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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