Suicide by Cop Among Officer‐Involved Shooting Cases
Bibliographic record
Abstract
The frequency and characteristics of suicide by cop cases (SBC) among a large (n = 707) nonrandom sample of North American officer-involved shootings (OIS) were investigated. "Suicide by cop" is when a subject engages in behavior which poses an apparent risk of serious injury or death, with the intent to precipitate the use of deadly force by law enforcement against the subject. Thirty-six percent of the OIS in this sample were found to be SBC with high interrater agreement (intraclass correlation coefficient = 0.93) for category classification. SBC cases were more likely to result in the death or injury of the subjects than regular OIS cases. Most SBC cases were spontaneous, but had clear verbal and behavioral indicators that occurred prior to, and during the event. Findings confirm the trend detected in earlier research that there was a growing incidence of SBC among OIS. SBC individuals had a high likelihood of possessing a weapon (80%), which was a firearm 60% of the time. Half of those with a firearm discharged it at the police during the encounter. Nineteen percent simulated weapon possession to accomplish their suicidal intent. Other findings highlight the histories and commonalities in this high risk group.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".