Intellectualizing whiteness as a response to campus racism: some concerns
Bibliographic record
Abstract
This paper is rooted in the reactions of our university community to a racist poster that appeared on our campus. It presents a critique of tendencies to intellectualize whiteness in depoliticized forms as a response to acts of racism, tendencies that work to centre “good whiteness” in unconscious ways, obstructing opportunities for a more robust and determined politics of anti-racism. We structure the paper around three concerns: the first, a conceptual concern, contests the notion that an ethically admirable or desirable response to racism can ever be sought through an appeal to intellectualization aimed at passively healing the intellectualizer; the second, a speculative concern, considers how particular modes of intellectualizing whiteness can seduce people into thinking they have taken a stand against racism where no such stand exists; the third, a practical concern, considers what a more worthwhile response to acts of racism might entail given the criticisms we identify.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.064 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".