Can Precision Policing Reduce Gun Violence? Evidence from “Gang Takedowns” in New York City
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
Abstract During the last decade, while national homicide rates have remained flat, New York City has experienced a second great crime decline, with gun violence declining by more than 50 percent since 2011. In this paper, we investigate one potential explanation for this dramatic and unexpected improvement in public safety—the New York Police Department's shift to a more surgical form of “precision policing,” in which law enforcement focuses resources on a small number of individuals who are thought to be the primary drivers of violence. We study New York City's campaign of “gang takedowns” in which suspected members of criminal gangs were arrested in highly coordinated raids and prosecuted on conspiracy charges. We show that gun violence in and around public housing communities fell by approximately one third in the first year after a gang takedown. Our estimates imply that gang takedowns explain nearly one quarter of the decline in gun violence in New York City's public housing communities over the last eight years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 it