The Increase in COVID-19 Cases is Associated with Domestic Violence
Bibliographic record
Abstract
Numerous anecdotal reports suggest that domestic violence has increased globally since the COVID-19 pandemic, but rarely are there cross-country empirical support for this claim. Using two unique datasets which comprises official domestic violence data from Southern China (N = 152 daily data points from January 1st to May 31st, 2020) and Google Trends data across four English-speaking countries (i.e., Australia, Canada, the United Kingdom, and the United States; N = 728 daily data points from January 1st to June 30th, 2020), we test the association between daily confirmed cases of COVID-19 and daily reports of domestic violence. We find that daily new cases are positively associated with domestic violence in Australia, Canada, the United Kingdom, and the United States, but not in China. However, one nuance of our findings in China is that this association is lagged. We speculate that it is because that China is the first to experience the pandemic during which many people were not acutely aware of or affected by COVID-19. These findings suggest that the COVID-19 health toll is beyond its direct costs on its infectees and provide insights into social policies on public health crises. Governments need to balance their COVID-19 responses with corresponding assistance toward women and children who might be at risk of domestic violence in this difficult time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".