Unhappy Confessions: The Temptation of Admitting to White Privilege
Bibliographic record
Abstract
Admissions of white privilege and/or racism are common among white anti-racists and others who want to combat their racism. In this article, I argue that because such admissions are conscious attempts to address unconscious habits, they are unhappy speech acts and contrary to their implied aims. Admissions of white privilege or racism can be conceptualized as Foucauldian confessions that are pleasurable to enact but ultimately reinforce white people’s feelings of goodness and allow them to avoid addressing this racism. I ground my argument in Shannon Sullivan’s analysis of white privilege and Sara Ahmed’s critique of confessions of racism/privilege to show that in addition to doing no anti-racist work at the moment of saying, these confessions actually reify white privilege deeper into the unconscious and make it harder to address. Sullivan’s work, I conclude, offers white people a more productive way forward than their unhappy performative declarations of privilege. A white person’s understanding of her confessing habit cannot break this habit, but it might orient her toward examining what sorts of anti-racist moves do work.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".