“Just Be White (JBW)”: Incels, Race and the Violence of Whiteness
Bibliographic record
Abstract
Largely operating online, incels are predominantly male individuals who are frustrated by their involuntary celibacy—their inability to get a romantic or sexual partner. Their worldview is grounded in hostile sexism largely directed at women and shared contempt for mainstream dating standards and feminism. Some incels posit that they can undertake specific racially-defined actions (i.e., skin bleaching, lying about one's ethnicity, cosmetic surgery) to increase their access to women by appearing more white and, hence, more desirable. By thematically analyzing 10 online incel forums on the topic of race, this research identifies the role of race as a sustaining facilitator of networked misogyny and white supremacy. Despite these racialized efforts to appear more white, many incels conclude that these efforts to change themselves are largely ineffective in increasing their access to women. Seeing as over half of incels seek counseling and social work services, this research puts forth several implications for social workers supporting incel clients and highlights the importance of understanding the role that race plays in incel clients’ rhetoric—not only in reproducing racism, but also in provoking violence-sustaining affects (e.g., anger, disappointment, resentment) that generate a shared sense of betrayal and reinforce gender-based violence.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".