School Shootings in the United States: 1997–2022
Bibliographic record
Abstract
OBJECTIVES: Gun violence in the United States is a public health crisis. In 2019, gun injury became the leading cause of death among children aged birth to 19 years. Moreover, the United States has had 57 times as many school shootings as all other major industrialized nations combined. The purpose of this study was to understand the frequency of school-related gun violence across a quarter century, considering both school shootings and school mass shootings. METHODS: We drew on 2 publicly available datasets whose data allowed us to tabulate the frequency of school shootings and school mass shootings. The databases contain complementary data that provide a longitudinal, comprehensive view of school-related gun violence over the past quarter century. RESULTS: Across the 1997-1998 to 2021-2022 school years, there were 1453 school shootings. The most recent 5 school years reflected a substantially higher number of school shootings than the prior 20 years. In contrast, US school mass shootings have not increased, although school mass shootings have become more deadly. CONCLUSIONS: School shootings have risen in frequency in the recent 25 years and are now at their highest recorded levels. School mass shootings, although not necessarily increasing in frequency, have become more deadly. This leads to detrimental outcomes for all the nation's youth, not just those who experience school-related gun violence firsthand. School-based interventions can be used to address this public health crisis, and effective approaches such as Multi-Tiered Systems of Supports and services should be used in support of students' mental health and academic and behavioral needs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.000 | 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".