SCÉES Who gets Market Supplements? Gender Differences within a Large Canadian University
Bibliographic record
Abstract
This study examines the gender pay gap among university faculty by analyzing gender differences in one component of faculty members’ salaries – “market premiums. ” The data were collected during the Fall of 2002 using a survey of faculty at a single Canadian research uni-versity. Correspondence analysis and logistic regression analysis were performed in order to identify the characteristics related to the award of market premiums and whether these characteristics account for gender differences. The correspondence analysis produces a two-factor solution in which the second axis clearly opposes faculty who receive market premiums to those who do not. Gender is strongly related to this factor, with the female category on the side of the axis associated with the absence of market premiums. The results of the logistic regres-sion confi rm that fi eld of specialization, frequency of external research contracts, faculty members ’ values and attitudes towards remunera-tion and seniority within rank are all related to the award of market premiums, as hypothesized. However, women were still almost three times less likely than men to have been awarded market premiums af-ter controlling for these relationships. Overall, the results suggest that within a collective bargaining context, reindividualization of the pay determination process — notably, the payment of market premiums to faculty — may reopen pay differences by gender.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".