Bibliographic record
Abstract
I examine whether the feminization of occupations leads to wage declines over time, revealing how gendered labour is valued in Canada. A significant portion of the Canadian gender pay gap is attributed to men and women working in different occupations. Typical strategies to reduce gender pay inequality have focused on encouraging women to enter male-dominated fields. However, the Canadian labour market remains highly gender-segregated, raising concerns about the effectiveness of current equity policies. I apply devaluation theory, which suggests that occupations predominantly filled by women face discrimination due to the social undervaluation of women’s work. While research from other countries shows that wages decrease as occupations become more female-dominated, Canadian studies are limited on this topic. I address this gap by harmonizing 30 years of occupation-by-industry census data to create a unique dataset. Using this data, I conduct a fixed-effects regression analysis with a 5-year lag to explore the relationship between increased female representation and wage declines. My model controls for human capital factors, including education, skill, tenure, and industry sectors, while considering the impact of unionization trends on wages. My findings bring a fresh perspective to gender discrimination at the occupation level in Canada, contributing to Diversity, Equity, and Inclusion (DEI) debates and providing insights into the structural factors that perpetuate gender disparities. If wage declines are linked to increased female participation, current strategies may fail to close the gender pay gap. Conversely, if no significant effect is found, it will offer valuable insights for comparative studies.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".