School shootings during 2013–2015 in the USA
Bibliographic record
Abstract
BACKGROUND: Data on the factors associated with school shootings in the USA are limited. The public conversation has often suggested several factors that may be linked to these events, however with little empirical support. Aiming to fill this gap, we describe the characteristics of school shooting incidents in the USA between 2013 and 2015 and explore whether four factors that represent domains of firearm policy, educational policy and epidemiological risk factors for intentional firearm injuries-background check (BC) policies, per capita mental health expenditures (MHE), K-12 education expenditure (KEE) and urbanicity-were associated with school shootings during this period. METHODS: We searched LexisNexis, a newspaper and broadcast media databases for school shooting incidents from 1 January 2013 to 31 December 2015. Presence of BC laws was extracted from legal information in LexisNexis. State-level covariates of per capita MHE (2013), KEE (2013) and urbanicity (2010) rates were obtained from publicly available data sources. We used negative binomial regression models accounting for clustering by state to explore unadjusted associations between the BC laws, state-level covariates and school shootings to report IRR and 95% CI. RESULTS: We documented 154 school shootings (35, 55 and 64 each year). In unadjusted models, BC for firearm purchase (IRR=0.55, 95% CI 0.39 to 0.76), ammunition purchase (IRR=0.11, 95% CI 0.05 to 0.27), log per capita MHE (IRR=0.58, 95% CI 0.37 to 0.90), log per-capita KEE (IRR=0.09, 9% CI 0.02 to 0.29) and urbanicity (IRR=0.97, 95% CI 0.96 to 0.99) were associated with school shooting. CONCLUSIONS: School shootings are less likely in states with BC laws, higher MHE and KEE, and with greater per cent urban population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".