Stay at home if you can: <scp>COVID</scp>‐19 stay‐at‐home guidelines and local crime
Bibliographic record
Abstract
Abstract Government responses to the COVID‐19 pandemic had an unprecedented impact on mobility patterns with implications for public safety and crime dynamics in countries across the planet. This paper explores the effect of stay‐at‐home guidelines on thefts and robberies at the neighborhood level in a Latin American city. We exploit neighborhood heterogeneity in the ability of working adults to comply with stay‐at‐home recommendations and use difference‐in‐differences and event‐study designs to identify the causal effect of COVID‐19 mobility restrictions on the monthly number of thefts and robberies reported to police across neighborhoods in Montevideo (Uruguay) in 2020. Our results show that neighborhoods with a higher share of residents with work‐from‐home jobs experienced a larger reduction in reported thefts in relation to neighborhoods with a lower share of residents with work‐from‐home jobs. In contrast, both groups of neighborhoods experienced a similar reduction in the number of reported robberies. These findings cast light on opportunity structures for crime but also on how crime during the pandemic has disproportionately affected more vulnerable areas and households.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".