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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".