Injury research explains conflicting violence trends
Bibliographic record
Abstract
Police records should not be used to measure violence This commentary explains conflicting trends in violence over the past decade as derived from the two official sources of information: household crime surveys designed to identify citizens’ experience of crime and police data. Police records, rather than representing a reliable measure of trends in violence, are a product of police activity—increasingly prompted by better surveillance and targeting, increased numbers of police, and changes in recording practices. Injury data from emergency departments are an objective measure of harm and should be used to target local violence prevention resources. Measuring interpersonal violence is an important objective: there is keen interest in trends among policy makers; a broad range of criminal justice, community safety, and victim organizations; the media; and among the public. However, the two traditional and high profile violent crime measures—annual crime surveys and police data—have shown conflicting trends.1,2 According to the British Crime Survey (BCS), for example, violence in England and Wales fell by 36% from a peak in 1995 to 2004.1 In contrast, violent offences recorded by the police in England and Wales almost doubled from 1996 to 2004.2 Similarly in the US, as convictions for violent offences have increased, reflecting greater police activity, so violence rates have fallen.3 Public health interest in violence is reflected in national electronic injury surveillance systems which have been developed, for example in Australia (Basic Routine Injury Surveillance System), Canada (Canadian Hospitals Injury Reporting and Prevention Programme (CHIRPP)), and the US, but these systems are not designed to complement police and national crime survey violent crime statistics or to contribute to prevention.4–7 This is because it has not been recognized until recently that a principal contribution of health services to violence prevention is information about those many—even …
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 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".