Pain, Injury, Mortality: Police Confront Critical Incidents
Bibliographic record
Abstract
Previous research has shown that the law enforcement occupation is a dangerous profession that has the highest violent victimization rate in the United States (Fridell, Faggiani, Taylor, Brito, & Kubu, 2009). This descriptive study aims to add to the growing body of literature on victimization of police officers by answering the central research question: What are the characteristics of victimization incidents of on-duty law enforcement officers? Specific demographics of interest include; sex of the officer, method of harm used against the officer, incident location, and responding call type. A content analysis was performed on news articles reporting incidents of on-duty law enforcement fatalities and injuries (n=50), in which characters gathered from the articles were recoded to numbers for quantitative analysis. Analysis of data suggests that male officers are more likely to be victimized while on-duty. Gunfire is the method of harm most likely used to victimize officers. A roadway is the location where an incident of victimization will most likely occur, during other types of calls beyond warrant services, traffic stops, domestic disturbances, and suspicious persons. This research can lead to future more detailed research and causal analysis.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".