How virtual relationality enables the incel collective, its narrative and violence
Bibliographic record
Abstract
Involuntary celibates (incels) are individuals who feel alienated from society because of their perceived inability to attract women. They share a narrative which valorises violence as a means to restructure society according to misogynistic ideals. Since promoting a radical misogynistic ideology through violence is legally prohibited and socially unacceptable, it is necessary for incels to hide out online where virtual mediums promise anonymity. Virtual relationality (VR) allowed unorganised individuals with a shared grievance against mainstream societal values to develop into a secretive collective. VR means that individuals connect in chatrooms and internet forums where they exchange interpretations of a narrative which presents their social situation as oppressive and dehumanising. They blame ‘modernist’ values and social practices for their ills and share perspectives of how they should interact with the world. These perspectives frequently justify or condone acts of physical violence. Relating in the virtual world gave these individuals’ shared grievances a sense of coherence and allowed for an amplification of the influence of their violent acts by affirming the value of extremist ideas. VR turned individuals without consistent preferences and a unifying organisational medium into a ‘hydra’ with a felt global presence. The virtual nature of incels complicates responses by 1) enabling the collective to have global reach, 2) allowing it to function without an organised body or institutional structures, 3) making it difficult to identify individual incels, and 4) making it difficult to tell when incels pose a genuine security threat. These realities make it crucial to develop a fuller understanding of how the incel collective functions online.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".