Incel Ideology and Terrorism: Policy Responses, Organizational Deradicalization Approaches and Online Self-Deradicalization
Bibliographic record
Abstract
This dissertation examines the emergence, recognition, and response to incel violence through three interconnected studies that traces the evolution of terrorism policy from initial threat identification through intervention development to self-deradicalization processes. Using incel violence in Canada as a case study, the research addresses fundamental questions about how societies adapt their institutional responses to novel forms of domestic terrorism while revealing significant gaps in understanding and capacity to address online extremist movements. The research employs a mixed-methods approach across three distinct but complementary studies. Study 1 utilizes theory-building process tracing to examine how Canadian officials learned to recognize and prosecute incel violence as terrorism following two attacks in Toronto. Study 2 conducts a systematic review using the PICO framework to identify and evaluate deradicalization programs specifically designed to address incel extremism. Study 3 employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine pathways through which individuals exit incel ideology without formal intervention, analyzing survey data from 137 participants recruited from Reddit communities. Collectively, the studies illustrate both the adaptability and limitations of existing counterterrorism infrastructure when confronted with novel threats. While legal and policy frameworks demonstrated capacity for evolution through learning processes, organizational and intervention systems showed significant gaps that leave individuals dependent on informal support networks and self-directed change processes. The research reveals the centrality of online spaces in contemporary extremism and deradicalization, suggesting that effective responses must leverage digital platforms for intervention rather than simply viewing them as places for radicalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".