“They Bring Standards of Academic Excellence Down”
Bibliographic record
Abstract
Calls to hire more diverse faculty members in South African and Canadian universities have long standing histories. The pace of implementation of proposals to appoint more Black and women faculty members was slow. It was partly pressures from the #RhodesMustFall student movement in South Africa (2015) and renewed calls to address anti-Black racism in Canada post the murder of George Floyd in the United States (2020) that prompted post-secondary institutions in these countries to take concrete action towards instituting campus wide transformations to address questions of equity, diversity, and inclusion. Informed by the Othering theory and using thematic analysis, this paper critically examines social media users’ rebuttals to the hiring of more Black and women faculty members at universities in South Africa and Canada. This paper argues that the racist and sexist framing of Black and women faculty as the inferior ‘other’ potentially has negative consequences on the mental health of the aforementioned groups. This article also challenges ahistorical analyses that neglect critical examinations of racist and sexist systemic barriers that women and Black faculty contend with when applying for academic positions. Further, this paper exposes the limitations of the logic that assumes that merit-based hiring is necessarily inimical to sustaining standards of academic excellence.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".