Racialized leaders leading Canadian universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As of our most recent census data, racialized people comprise 22.3% of the Canadian population (Statistics Canada, 2016). Canadian universities have espoused commitments to diversity and inclusion but there has long been a gap between the rhetoric and practice. Research has demonstrated that under-representation is a problem at all levels of academia but particularly within the senior ranks. Drawing on an original dataset representing 324 senior university leaders, this study will empirically map the demographic composition of academic leaders across Canada, including presidents, vice-presidents, assistant vice-presidents, associate vice-presidents, provosts, and vice-provosts. Our findings suggest that racialized people in leadership are under-represented compared with their presence in the university population—consistent with the pyramid of exclusion where the representation of racialized people decreases as we move up the ranks. Taking a systems perspective informed by our critical ecology model we examine the overlapping societal-, organizational-, and individual-level mechanisms that impede the advancement of racialized people into leadership positions at universities in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it