Appendix to Chapter 7, “Academic Gender Wage Gaps in Canada,” in “Glass Ceilings and Ivory Towers : Gender Inequality in the Canadian Academy”
Bibliographic record
Abstract
This document is an appendix to Chapter 7, “Academic Gender Wage Gaps in Canada,” in Glass Ceilings and Ivory Towers: Gender Inequality in the Canadian Academy. It provides detailed descriptive and regression statistics tables for the data discussed in the chapter. About the book: Even as Canadian universities suggest their gender issues have largely been resolved, many women in academia tell a different story. Systemic discrimination, the underrepresentation of women in more senior and lucrative roles, and the belief that gender-related concerns will simply self-correct with greater representation add up to a serious gender problem. Although widely acknowledged, reliable data demonstrating these problems is elusive. Glass Ceilings and Ivory Towers fills this research gap with a cross-disciplinary, data-driven investigation of gender inequality in Canadian universities. Research presented in this book reveals, for example, that women are more likely to hold sessional teaching positions and to face difficulties obtaining funding. They are also poorly represented at the upper echelons of the professoriate and must contend with a gender pay gap that widens as they move up the ranks. Contributors consider the daily grind of academic life, social, structural, and systemic challenges, and the gendered dynamics of university leadership, all with an eye to laying the groundwork for practical and meaningful institutional change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.264 | 0.054 |
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".