Bibliographic record
Abstract
In this article I will show two things: first, that the labour market is still very divided with respect to gender and, second, that the material impact of this division differs sharply by level of education. Among occupations that require the least education, women pay a very high price for this gender based division of employment. In contrast to occupations where more education is needed, those requiring the least education show a huge difference in wages according to whether they are predominantly male or predominantly female. This difference is a widespread phenomenon that favours so-called male occupations. The corresponding pay gap, in favour of men, in occupations requiring a high school diploma (Secondary V in Quebec) or less, is shrinking only slightly, whereas the gaps between men and women in occupations requiring more education are clearly closing. The article then demonstrates that three often mentioned options for action, at present, offer little hope to counter that particular phenomenon: Quebec’s Pay equity act application, collective bargaining and internal promotion. Yet, this problem still affects approximately 500,000 women, after 25 years of equal access programs and close to 15 years of implementation of the Pay Equity Act. Employment equity programs are the most promising initiatives, provided that they find their way into the affected employment sectors.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".