What Do Incels Want? Explaining Incel Violence Using Beauvoirian Otherness
Bibliographic record
Abstract
Abstract In recent years, online “involuntary celibate” or “incel” communities have been linked to various deadly attacks targeting women. Why do these men react to romantic rejection with not just disappointment, but murderous rage? Feminists have claimed this is because incels desire women as objects or, alternatively, because they feel entitled to women's attention. I argue that both of these explanatory models are insufficient. They fail to account for incels’ distinctive ambivalence toward women—for their oscillation between obsessive desire and violent hatred. I propose instead that what incels want is a Beauvoirian “Other.” For Beauvoir, when men conceive of women as Others, they represent them as simultaneously human subjects and embodiments of the natural world. Women function then as sui generis entities through which men can experience themselves as praiseworthy heroes, regardless of the quality of their actions. I go on to give an illustrative analysis of Elliot Rodger's autobiographical manifesto, “My Twisted World.” I show how this Beauvoirian model sheds light on Rodger's racist and classist attitudes and gives us a better understanding of his ambivalence toward women. It therefore constitutes a powerful and overlooked theoretical alternative to accounts centered on objectification and entitlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".