Preface and Acknowledgments
Bibliographic record
Abstract
This book is a collaborative effort by a team of seven Canadian critical race and social justice scholars.As part of our initial research planning, each of us elected to pursue an area of particular concentration.For example, Enakshi Dua's interest in equity policies and practices led her to study university equity offices; Howard Ramos and Peter Li chose to study income disparities and measures of achievement; Frances Henry, Carl James, Audrey Kobayashi (and, in an earlier phase, Carol Tator) conducted dozens of face-to-face interviews with racialized and Indigenous faculty; and Malinda Smith researched social science disciplines and the role of unconscious or implicit bias.After a presentation on the then recently released Racism in the Can adian University: Demanding Social Justice, Inclusion and Equity (Henry and Tator 2009a) at the Congress of the Humanities and Social Sciences, we had an animated discussion about the need for a national study, because of the scarcity of data on the number of racialized and Indigenous faculty in universities, pay equity structures, curriculum, climate, or incidents of discrimination, harassment, and bullying.For example, neither Statistics Canada nor the Canadian census publishes data on the percentages of racialized minorities in Canadian universities, either as faculty, staff, or students.While provincial governments publish data on student enrollment in universities by gender, and some based on Indigenous status, none of these governments publishes data for racialized minorities.Further, there are no data on the effectiveness of mechanisms, such as employment equity, affirmative action, and antidiscrimination policies.Thus, we felt that a large-scale national study was needed, and this book brings together four years of research on racism, racialization, and Indigeneity in the university.Reflecting the interdisciplinary field of critical race and Indigenous studies, our research team is composed of senior scholars from the disciplines of anthropology, education studies, geography, political science,
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.185 | 0.101 |
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".