Schools as a Safety-net: The Impact of School Closures and Reopenings on Rates of Reporting of Violence Against Children
Bibliographic record
Abstract
Ongoing school closures and gradual reopenings have been occurring since the beginning of the COVID-19 pandemic. One substantial cost of school closure is breakdown in channels of reporting of violence against children, in which schools play a considerable role. There is, however, little evidence documenting how widespread such a breakdown in reporting of violence against children has been, and scant evidence exists about potential recovery in reporting as schools re-open. We study all formal criminal reports of violence against children occurring in Chile up to December 2021, covering physical, psychological, and sexual violence. This is combined with administrative records of school re-opening, attendance, and epidemiological and public health measures. We observe sharp declines in violence reporting at the moment of school closure across all classes of violence studied. Estimated reporting declines range from -17% (rape), to -43% (sexual abuse). While reports rise with school re-opening, recovery of reporting rates is slow. Conservative projections suggest that reporting gaps remained into the final quarter of 2021, nearly two years after initial school closures. Our estimates suggest that school closure and incomplete re-opening resulted in around 2,800 `missing' reports of intra-family violence, 2,000 missing reports of sexual assault, and 230 missing reports of rape against children, equivalent to between 10-25 weeks of reporting in baseline periods. The immediate and longer term impacts of school closures account for between 40-70% of `missing' reports in the post-COVID period.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".