Weaponized Subordination: How Incels Discredit Themselves to Degrade Women
Bibliographic record
Abstract
In this article, I analyze weaponized subordination, wherein men strategically use their perceived subordinate masculine status to legitimate their degradation of women. I draw on a qualitative analysis of 9,062 comments made on a popular involuntary celibate (“incel”) discussion board. Incels are an online community of men who define themselves by their inability to participate in heterosexual sex/relationships. Incel forums are characterized by self-loathing, anger, and misogyny, with several incels having committed murders (e.g., Elliot Rodger). I first detail the type of subordination incels argue they experience—a social bias in favor of attractive people they call lookism. Next, I explain how incels perceive themselves as permanently subordinated “failed men.” I then demonstrate how incels weaponize their subordination, using their perceived subordinate status to justify their misogyny. Findings are discussed in relation to hybrid masculinity and conceptualizations of subordinate masculinities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".