Calling the police: theoretical insights and practical implications
Bibliographic record
Abstract
Studying whether, why, and how people call the police when they experience or witness a crime is crucial for understanding crime patterns, improving the accuracy of crime data, and shaping effective policing and criminal justice responses. Police-recorded crime statistics rely on public reporting, meaning that unreported crimes contribute to the ‘dark figure of crime’, distorting crime estimates and ultimately affecting practice and policy decisions. Understanding reporting behaviors helps identify and address barriers to reporting, including disparities across population groups and locations. This knowledge is essential for supporting evidence-based policing, improving victim support, and enhancing crime prevention strategies. This special collection comprises nine articles that advance theoretical explanations of crime reporting behavior and examine how calls for service shape demand for police services. The articles explore various aspects of crime reporting, including how perceptions of courts influence reporting behavior, how reporting channels impact victims’ satisfaction with the police, and how neighborhood characteristics such as racial composition, economic conditions, and mental health affect crime reporting propensities. Additionally, the collection contributes to understanding crime reporting behaviors for emerging forms of cyber-enabled crime such as cyberstalking and romance fraud. Finally, it explores spatial and temporal patterns of calls for service and proposes ways to better quantify police demand, enabling more informed management and prioritization of resources.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".