Bibliographic record
Abstract
We perform an empirical investigation of the socioeconomic determinants and consequences of all mass shootings in the U.S. from 2000 to 2015. We first manually search for and collect information on perpetrators. We find that approximately 40% (45%) of shooters were in financial distress (unemployed or out of the labor force) at the moment of the shooting, suggesting that economic distress may trigger rise in shooting. We then investigate the economic consequences of mass shootings. In order to obtain the causal effects of shootings, we exploit the inherent randomness in the success or failure of mass shootings. We find that, on average, successful mass shooting have economically significant negative effects on targeted counties' employment and earnings. As well, successful mass shootings decrease housing prices and consumer confidence and increase absenteeism. Last, we employ an instrumental variable strategy and show that national media coverage of mass shootings exacerbate their local economic consequences.
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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 | 0.007 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".