Diversifying the Professoriate in Canadian Academe: A Case Study of Search Processes and Outcomes in a Faculty of Science
Bibliographic record
Abstract
This paper discusses the findings of a case study that sought to answer the question of whether and how an institutional faculty hiring policy that has codified a set of EDI best practices can be effectively deployed by search committees to foster more equitable hiring processes and diverse hiring outcomes. The research involved 23 searches implemented over a two-year period within the Faculty of Science of a Canadian research-intensive university. Using a mixed-methods survey design, the study sought to answer the research question by (1) analyzing the self-reported perceptions of the search committee members, including identifying any differences across gender and racial identity of committee members, and (2) analyzing the self-reported experiences of the longlisted candidates, including new hires. The study results suggest that codifying EDI best practices may be a ‘necessary but insufficient’ condition to advancing inclusive excellence in faculty hiring. While the practices on balances were perceived to be effective in improving equitable processes, their impacts on improving diverse outcomes were mixed. The study revealed several opportunities to clarify and enhance competencies to deploy key practices, and several insights, which have implications for fostering more equitable faculty hiring and diversifying the professoriate with respect to gender and racial representation.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".