Incels and gender inequality: changing tides in defining the far right
Bibliographic record
Abstract
ABSTRACT Scholars have debated how to define far-right groups. Literature has suggested that far-right groups are exclusionary, nativist, xenophobic, anti-democratic and that they do not believe in equality. However, how do we define groups that have ambiguous political affiliations, such as incels, who still hold extreme exclusionary beliefs? We analyze 9062 comments on the Incels.is forum during a three-month period in 2019. We found that incels challenge what it means to be far right in three main ways. First, unlike other far-right groups, incels determine group boundaries based on the gender, sexual identities, and experiences of potential members. Second, incels are racially and nationally diverse, not concentrating efforts in one country, and third, incels do not propose violence on politicians or a political party, but instead propose violence against women and members of the public (“normies”) that support feminism. We argue that because of these differences, gender inequality informs incels’ exclusionary practices, instead of the nationalism and xenophobia which other far-right groups tend to emphasize. We propose taking adherence to traditional values and misogyny seriously as an aspect the far right and support calls to broaden far-right definitional frameworks.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".