Teaching to Fear: How Islamophobia is Perpetuated in Criminology and Adjacent Disciplines
Bibliographic record
Abstract
Throughout American law enforcement history, a persistent pattern has emerged, emphasizing marginalized groups. This emphasis extends to higher education, notably seen in the proliferation of Terrorism and Homeland Security courses post-9/11 in criminology and related fields. These courses predominantly focus on the ‘Islamic threat’ to America, reflecting broader biases in counter-terrorism policies. Such biases overlook other forms of terrorism, especially right-wing extremism. We analyzed 382 syllabi from American higher education institutions to assess this bias. Our analysis reveals a concerning prevalence of misinformation in these courses. Educators hold a pivotal role in rectifying this imbalance, shaping the perceptions of future criminologists. Addressing this bias is crucial to providing a comprehensive understanding of terrorism threats and acknowledging all forms of extremism equally. It underscores the urgency for educators to present accurate and inclusive portrayals of terrorism in their curriculum.
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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 | 0.001 | 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 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".