Trends in Officer‐involved Firearm Deaths in Oklahoma from 2000 to 2015
Bibliographic record
Abstract
The purpose of this study was to collect data and disseminate trends in officer-involved firearm deaths in Oklahoma from 2000 to 2015. The Oklahoma Office of the Chief Medical Examiner (OCME) database was searched for civilian decedents with gunshot wounds inflicted by law enforcement officers and officer decedents with gunshot wounds inflicted by civilians. Five decedents were law enforcement officers, while 274 decedents were civilians. The number of civilian decedents throughout the study followed a quadratic trend. Civilian decedents were most commonly males (95%) between the ages of 20 and 39 (64%), had one or two gunshot wounds (46%), and had an increasing number of gunshot wounds over time. Postmortem toxicology testing most commonly detected ethanol, methamphetamine, cocaine, and PCP. Efforts toward increased tracking by various agencies and more scientific studies like this are needed to facilitate future analysis of trends in officer-involved firearm deaths.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".