Night After Night, My Heartbeat Shows the Fear: Examining Predictors of Fear of Crime in the Nonurban Australian Context
Bibliographic record
Abstract
Research on fear of crime (FOC) is well established in urban contexts. However, few studies explore how common predictors of FOC operate within nonurban environments. This study examines typical predictors of FOC within the nonurban context of Roma, Queensland, and specifically explores mental health as a predictor in this context. Using survey data, key findings indicate that a number of individual and ecological level predictors, such as gender, prior victimization, social cohesion, and social disorder, remain consistent with previous literature on urban contexts. However, these results counter recent findings indicating that gender does not predict FOC in the nonurban context. Interestingly, the results also indicate that mental health is not a predictor of FOC. These findings present implications for fear reduction strategies and future research in nonurban contexts.
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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 | 0.002 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".