BOOK REVIEW Title: Whose University Is It, Anyway? Power and Privilege on Gendered Terrain
Bibliographic record
Abstract
As a visible minority educator, I am always interested in analyzing the personal tensions, environmental barriers, and imaginative possibilities that exist when embracing equity- and diversity-conscious approaches to teaching, learning, and interacting in higher education institutions. In Whose University Is It, Anyway? Power and Privilege on Gendered Terrain, these areas of tension are treated as creative spaces to draw from the work of feminists who use this site as a site for power and new knowledge production. Whose University Is It, Anyway? Power and Privilege on Gendered Terrain motivates the reader to question whether equity is available to all individuals whose identity is interwoven with gender, race, ethnicity, disability, social class, and religion and within various situational subjects (e.g., student, teaching assistants, faculty, and administrators) in a Canadian context. The book has four parts; the first section illustrates the challenges facing racialized minority women, Aboriginal women, and women with disabilities. The second section explores various experiences such as those related to racialized minority scholars, and women who have experienced violence, queer and gendered individuals. The third section provides narratives of women in various academic roles (teaching assistants, administrative assistants, department chairs, and non-tenure-track faculty). The diversity of the
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.023 |
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".