To What Extent Have Incels been Recognized as a Threat in Need of Securitization? : In the Case of the United States, Canada and Europe
Bibliographic record
Abstract
Involuntary celibates (Incels) are an online community of men who struggle to find sexual and romantic relationships. Some members of the Incel community have increasingly become extreme misogynists and have committed lethal attacks across North America and Europe in response to their sexual frustration and loneliness. Scholars have argued that there is a lack of recognition by political and judicial actors of the potential security threat that Incels pose. This lack of recognition could subsequently result in a lack of securitization of the threat and allow the community to continue to grow. The transnational consequences, through the use of online platforms for radicalization, if Incels are not recognized and securitized provide a relevant International Relations topic. The aim of this research was to determine whether relevant security agents in the US, Canada, and Europe had, over the past years, recognized Incels as a security threat and if a process of securitization had been initiated. The results were that only the EU could be determined to have initiated a securitization process of Incels, Canada’s security agent to a large degree recognized the threat Incels may pose but had not initiated a securitization process, and the US could not be determined to have fully recognized the threat nor initiated a process of securitization.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".