COVID-19 and Social Distancing Measures in Queensland Australia Are Associated with Short-Term Decreases in Recorded Violent Crime
Bibliographic record
Abstract
Since first diagnosed in late 2019, there have been more than 4 million confirmed cases of COVID-19 and more than a quarter of a million deaths worldwide. Not since the Spanish Flu in 1918 has the world experienced such a widespread pandemic and this has motivated many countries across globe to take a series of unprecedented actions in an effort to curb the spread and impact of the virus through the adoption of unprecedented domestic and international travel restrictions as well as stay-at-home and social distancing regulations. Whether these policies have altered criminal activity is an important question. In this study, we examine officially recorded violent crime rates for the month of March and April, 2020, as reported for the state of Queensland, Australia. We use ARIMA modeling techniques to compute six-month-ahead forecasts of common assault, serious assault, sexual offense and domestic violence order breach rates and then compare these forecasts (and their 95\% confidence intervals) with the observed data for March and April 2020. We conclude that by the end of April, rates of common, serious and sexual assault had declined to their lowest level in a number of years, and for serious assault and sexual assault the decline was beyond statistical expectations. The rate at which domestic violence orders were breached in Queensland has remained unchanged throughout the first two months of the pandemic.
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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 | 0.001 | 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 | 0.000 |
| 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".