Racism plays a disappearing act: discourses of denial in one anti-discrimination campaign in higher education
Bibliographic record
Abstract
This article responds to a university’s anti-discrimination campaign, ostensibly launched to combat racism. Taking up poststructural principles and anchored in anti-racism literature, we employ a discourse analysis to examine the truth productions about racism circulated by the campaign, and the subject positions to which they give rise. We analyse the consequences and possibilities for anti-racist action in the light of our argument that the campaign produced the university as an always already anti-racist space, becoming a means to an end to meaningful action. Through themes of belonging, denial, innocence, colour-blindness, and erasure, we demonstrate that the messaging of the campaign aligns with national narratives about Canadian society as free of racial inequity. We bring readers to consider how an anti-discrimination campaign effectively delegitimised the need for anti-racist action, imploring future initiatives to guard against re-inscribing the very forms of inequality they purport to disrupt.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.044 | 0.055 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".