Institutionalizing Inequalities in Canadian Universities: The Canada Research Chairs Program
Bibliographic record
Abstract
To position Canada as a world leader in the "knowledge-based" economy, in 2000, the Canadian government established a multi-million-dollar initiative to appoint 2,000 scholars as Canada Research Chairs (CRC). Women are seriously underrepresented among CRC research "stars," and no data are kept for other equity groups. Eight women initiated a complaint with the Canadian Human Rights Commission in 2003 in an attempt to remedy inequities, and a national discussion has ensued over excellence and equity. We provide a brief outline of the CRC Program and demonstrate how it perpetuates a narrow conception of innovation and excellence, which further institutionalizes inequalities for women and faculty members from other equity groups in Canadian universities. We describe the strategy of the human rights complaint and remedies negotiated in the settlement of 2006. We argue for a broader conceptualization and contextualization of "excellence," and for research not in the private, but the public good.
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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 | 0.053 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.070 | 0.028 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".