Sexual Orientation Wage Gaps across Local Labour Market Contexts: Evidence from Canada
Bibliographic record
Abstract
Mounting evidence suggests that sexual orientation matters in the labour market. Research in Canada points to a wage hierarchy not only by gender, but also by sexual orientation, with heterosexual men out-earning gay men, lesbians, and heterosexual women. While previous work has considered how human capital characteristics, occupation and industry of employment, and family status factor into the creation of these earnings disparities, little research has examined how residential concentration in large metropolitan areas factors into the creation of sexual orientation pay gaps. Drawing on the 2006 Census of Canada, this study investigates how sexual orientation wage gaps vary across geographic areas in Canada, documenting earnings disparities across the metropolitan/non-metropolitan divide as well as for Toronto, Montreal and Vancouver. We also evaluate whether the mechanisms contributing to wage gaps diverge across these contexts, focusing on how pay gaps differ across occupations, points in the earnings distribution, and sectors of employment. Our results show that pay gaps are highest in non-metropolitan Canada. The underlying components of wage gaps fluctuate across Canada, especially for gay men. Sexual orientation earnings penalties are reduced in public sector employment, even where private sector wage gaps are highest. These results suggest that local social and labour market contexts are associated with the earnings outcomes of sexual minorities.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".