Preface and Acknowledgments
Bibliographic record
Abstract
This book arose from four decades of scholarly interest in gendered patterns of work and more specifically in university-based academic work.In the late 1960s, as an undergraduate student at the University of Toronto, I discovered that Canadian men with eight years of elementary school education earned more money than women with a master's degree.This correlation, which I found in a Statistics Canada publication in my college library, inspired me to continue my formal education to the highest level.When I began my doctorate at the University of Alberta in 1972, there were no women in tenure-track positions in the Sociology Department.I thought I wanted to become a university professor but had few female role models, which encouraged me to study academic women for my doctoral research.In 2008, as a senior professor working in New Zealand, I decided to re-examine the academic gender gap by interviewing academic men and women in two different types of universities in that country.This book includes both of these studies, based on qualitative interviews from 1973 and 2008, as well as an extensive survey of the research on gender patterns of work, restructuring in academia, and the academic gender gap in the liberal states, including Australia, Canada, New Zealand, the United Kingdom, and the United States.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.207 | 0.099 |
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".