Essays on the size and sources of gender and sexual minority wage gaps in Canada
Bibliographic record
Abstract
Gender is a primary source of differentiation in the labour market. On average, women earn less than men even when they have the similar education, work full-time and in similar occupations. The availability of new data has also allowed researchers to explore whether labour markets are stratified by sexual orientation. This literature has found a hierarchy of earnings where heterosexual men earn the most, followed by gay men, lesbians and, lastly, heterosexual women. In other words, all men do better than all women but gay men are disadvantaged and lesbians are advantaged, relative to their heterosexual counterparts. To date, only a handful of studies have explored how sexual orientation shapes labour market outcomes in Canada. This is quite different from the large body of literature interested in the size and sources of the gender wage gap. The literature that has accumulated has tended to explore gender wage gaps at the aggregate level. For all the value of this research, wage gaps estimated at the aggregate level may conceal significant variation in the size and sources of wage disadvantage by age, education, field of study or occupation. The main objective of this dissertation is to disaggregate gender and sexual minority wage gaps to provide a more nuanced exploration of the size and sources of labour market stratification in Canada. In doing so, this dissertation will give greater meaning to residual or unexplained wage gaps estimated at the aggregate level and shed light on the mechanisms contributing to wage disadvantage. Two of the chapters in this dissertation also explore whether the size and sources of wage disadvantage have changed over time.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".