Offending White Men: Racial Vilification, Misrecognition, and Epistemic Injustice
Bibliographic record
Abstract
In this article I analyse two complaints of white vilification, which are increasingly occurring in Australia. I argue that, though the complainants (and white people generally) are not harmed by such racialized speech, the complainants in fact harm Australians of colour through these utterances. These complaints can both cause and constitute at least two forms of epistemic injustice (willful hermeneutical ignorance and comparative credibility excess). Further, I argue that the complaints are grounded in a dual misrecognition: the complainants misrecognize themselves in their own privileged racial specificity, and they misrecognize others in their own marginal racial specificity. Such misrecognition preserves the cultural imperialism of Australia’s dominant social imaginary—a means of oppression that perpetuates epistemic insensitivity. Bringing this dual misrecognition to light best captures the indignity that is suffered by the victims of the aforementioned epistemic injustices. I argue that it is only when we truly recognize difference in its own terms, shifting the dominant social imaginary, that “mainstream Australians” can do their part in bringing about a just society.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".