One is the loneliest number: Involuntary celibacy (incel), mental health, and loneliness
Bibliographic record
Abstract
Abstract Incels—a ragtag collection of young males who have rallied around their shared experience of romantic rejection—have slowly emerged as an online group of interest to researchers, no doubt as a result of several high-profile attacks. Much of this work has centered around incels’ dating experiences, sexual attitudes, and online forums. However, it is possible that their moniker, short for involuntary celibate, has resulted in an overemphasis on their sexual exclusion and frustration. Recent work has identified social isolation as a key aspect of inceldom, which may help explain why incels have responded negatively to romantic rejection. The present study thus sought to examine the role of social support and loneliness in experiences of rejection in a sample of incel (n = 67) and non-incel (n = 103) men. Results indicated that incels experience more feelings of loneliness and less social supports than non-incel men. Both of these variables were associated with multiple mental and relational health issues that incels also scored more highly on. Further, incels reported using more solitary and problematic coping mechanisms. These results suggest that incels may be missing a key buffer in sheltering them from the adverse effects of romantic rejection. It also extends previous findings highlighting the importance of attachment styles in differentiating incels from non-incels, which may perpetuate feelings of isolation. Implications for how this may relate to incel discourse and clinical interventions are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".