Beyond Fear of Crime: Toward a Reconceptualization of Emotional Responses to Threat in Urban Public Places
Bibliographic record
Abstract
What do social scientists communicate when they label a person or community fearful of crime? I examine the utility of “fear of crime” as a heuristic for representing emotional responses to threat. Applying Sklansky’s concept of cognitive burn-in, which describes the epistemic foreclosures that occur when schemas become entrenched, I argue that the use of “fear of crime” across domains including academic research and public policy ossifies a simplified framework for thinking about risk. This framework overstates the extent to which the public’s negative emotions in the street are directly crime related and conceals intersectional quality-of-life inequalities. Based on interview data, I theorize three emotional responses to threat: reactive fear, anticipatory fear, and anticipatory anxiety. These responses are socially stratified, with marginalized women disproportionately vulnerable to severe emotions. This typology disaggregates actual and prospective harms, distinguishes crime threats from social threats, and reveals the stratification of emotion and threat severity.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".