Bibliographic record
Abstract
This article uses data from the Survey of Labour and Income Dynamics (SLID) to investigate the extent to which factors not previously explored in the Canadian context account for wage differences between men and women. Like other studies using standard decomposition techniques and controlling for a variety of productivity-related characteristics, the results demonstrate that men still enjoy a wage advantage over women: women's average hourly wage rate is about 84% - 89% of the men's average. Unlike other studies, controls for work experience and job-related responsibilities are used. Gender differences in full-year, full-time work experience explain at most, 12% of the gender wage gap. Gender differences in the opportunity to supervise and to perform certain tasks account for about 5% of the gender wage gap. Yet despite the long list of productivity related factors, a substantial portion of the gender wage gap cannot be explained. Many studies rely on measures such as age or potential experience (= age minus number of years of schooling minus six) as a proxy for actual labour market. Neither of these measures account for complete withdrawals from the labour market nor for restrictions on the number of hours worked per week or on the number of weeks worked per year due to family-related responsibilities. The results show that proxies for experience yield larger adjusted gender wage gaps than when actual experience is used.
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 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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".