Defensive gun use: What can we learn from news reports?
Bibliographic record
Abstract
BACKGROUND: In the past decade, most people who buy and own guns are doing so for self-defense. Yet little is known about actual defensive gun use in the USA. METHODS: To discover what information newspaper articles and local news reports might add, we read the news reports of defensive use incidents assembled by the Gun Violence Archive. We examined a sample of more than a quarter of the incidents from 2019, the last year before the pandemic. We examined all cases from four months-January, April, July, and October. We created a typology of defensive gun use incidents. RESULTS: Of 418 incidents, in about half, the perpetrator was armed with a firearm. In almost 90% of the cases, the victim fired their firearm-315 perpetrators were shot and about half of them died. The average number of perpetrators shot per incident was 0.75; the average number of victims shot was 0.25. We estimate that in 2019 fewer than 600 potential perpetrators were killed in defensive gun use incidents that made the news. Among the thirteen categories of shooting were drug-related (4% of incidents), gang-like combat (6%), romantic partner disputes (11%), escalating arguments (13%), store robberies (9%), street robberies (5%), unoccupied vehicle theft (5%), unarmed burglaries (7%), home invasions (20%), and miscellaneous (6%). CONCLUSION: We believe the Gun Violence Archive dataset includes the large majority of news reports of defensive gun use-and especially those in which the perpetrator is shot and dies. Some of the strengths of using news reports as a data source are that we can be certain that the incident occurred, and the reports provide us with a story behind the incident, one usually vetted in part by the police with occasional input from the victims, perpetrator, family, witnesses, or neighbors. Defensive gun use situations are quite diverse, and among the various categories of defensive gun use, a higher percentage of incidents in some of the categories seemed far less likely to be socially beneficial (e.g., drug-related, gang-like, escalating arguments) than in others (e.g., home invasions).
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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 | 0.020 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.019 | 0.039 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".