CAN TOXIC MASCULINITIES BE DE-RADICALISED?: MAPPING THE DYNAMICS AND SPREAD OF INCEL IDEOLOGY ONLINE
Bibliographic record
Abstract
In recent years, male supremacist and anti-women formations have become increasingly prevalent online. In particular, considerable attention has been focused on the incel (involuntary celibate) community due to a number of high-profile mass killings in the United States, Canada and, more recently, the UK. Incel ideology is a misogynistic formation, whose male proponents blame women for their lack of sexual activity. It operates in the broader virtual space of the manosphere, a loose conglomerate of online communities spread across various digital platforms, which are united in their antipathy toward feminism, their belief in evolutionary psychology and their adherence to the Red Pill (a process of enlightenment, whereby one comes to understand the world as a liberal feminist conspiracy that disadvantages men). This research tracks the dynamic pathways by which incel ideology spreads within and across online communities, digital platforms and geographical spaces, with a view to better understanding processes of radicalization, including ‘algorithmic radicalization.’ We also explore the dynamic interplay between incel and alt-right rhetoric. Understanding the contagion dynamics of extremist ideas - how such ideas circulate, gain new audiences, and morph into new ones - is crucial to researchers, educators, platforms and security practitioners: However, theoretical and practical understanding of the online contagion of extremist ideologies is lacking. It is only by understanding these ‘pilling pipelines’ (Ging and Murphy 2021) that effective interventions can be developed, whether educational, technological, legal or platform-governance-related.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".