Duped: Examining Gender Stereotypes in Disengagement and Deradicalization Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In narratives around insurgencies, terrorism, and other forms of political violence, the media—and policymakers—frequently portray women as victims or unintelligent pawns of men. But these violent women get more media attention than their male counterparts because they are a shocking departure from gendered expectations of nurturing, peaceful women. However, even such narratives of deviance can reinforce societal stereotypes about women by emphasizing that they are emotional but not political, easily manipulated, often deranged, or simply unintelligent. Using in-depth interviews in Ireland and the United Kingdom with practitioners in counter terrorism (CT) and countering violent extremism (CVE), this paper argues that a failure to ask meaningful questions about women’s roles in extremist violence has reinforced gender stereotypes, leading to disengagement and deradicalization practices that ignore or downplay women’s importance in fostering violence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it