Calling the police: theoretical insights and practical implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| 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 it