‘Strange faces’ in the academy: experiences of racialized and Indigenous faculty in Canadian universities
Bibliographic record
Abstract
This paper is based on a larger qualitative study of exclusion and belonging as experienced by members of marginalized groups in the professions. The current analysis draws on a subsample of 13 racialized and Indigenous academics at Canadian universities to examine their experiences of both everyday racism – subtle, almost intangible micro-level interactions that convey messages of not fully belonging – and overt racism and colonialism. Overt experiences were less common, though intensely painful. Though in some ways they are more straightforward to address, as they are more obvious, they also consume considerable time and energy. Instances of everyday racism and colonialism were more common, often intricately interwoven with the very fabric of the institutional culture. Their cumulative nature is exhausting. Diversity initiatives, while popular in contemporary universities, are failing to approach equity, in that they deny the need for change in institutional cultures.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.067 | 0.031 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".