The Evolution of Male-Female Wages Differentials in Canadian Universities: 1970-2001
Bibliographic record
Abstract
In this paper, we use a unique data set containing detailed information on all fulltime teachers at Canadian universities over the period 1970 through 2001. The individual level data are collected by Statistics Canada from all universities in Canada and are used to analyze the evolution of male-female wage differentials of professors in Canadian universities. The long time series aspect of this data source along with the detailed administrative information allow us to provide a more complete and more accurate portrait of the wage gap than is available in most other studies. The results of a cohortbased analysis indicate that the male salary advantage among university faculty has declined for more recent birth cohorts. This has been driven not so much by an increase in the real salaries of female professors but from a cross cohort decline in the earnings of male professors and the fact that female professors have not experienced a similar cross cohort decline. Also important to note is the fact that the differences across cohorts appear to be permanent. There is no clear pattern of changes in these cohort differences with age.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".