Systemic discrimination and the Canada Research Chairs: diagnosis and treatment.
Bibliographic record
Abstract
If we are held more accountable for the appointment of women to the CRCs, if we take steps to ensure that nominations to the national CRCs committee are the result of a process that encourages applications directly from women candidates--and that includes a university-wide competition and peer review based on rigorous external assessment of candidates--and if we proactively and self-consciously seek to identify potential women candidates, we will have gone a long distance toward coupling equity and excellence in this program. If we add to these initiatives the training of search committees in successful strategies for identifying and recruiting women candidates, and the creation of employment conditions that acknowledge the reality of women's lives, including the reality of academic partners who need and merit academic positions, we will have gone an even greater distance toward success in appointing distinguished women to these prestigious Chairs. Moreover, we will have created practices that will serve us well in recruiting first-rate women to other academic appointments and that, in a virtuous circle, will improve the "pool" for future CRCs appointments.
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.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".