School shootings during 2013–2015 in the USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it