What Can We Make of Unsubstantiated Child Abuse Reports? A New Approach
Bibliographic record
Abstract
Abstract Only about a quarter of child abuse reports are ultimately substantiated, which has caused some concern among policymakers and the general public. But previous literature suggests that unsubstantiated and substantiated reports may not be much different from each other in terms of child outcomes. We present a Bayesian theoretical analysis of the data-generating process underlying maltreatment substantiation, and then take a new empirical approach by examining the statistical time-series relationship between substantiated and unsubstantiated reports. We show that the two series are cointegrated. This suggests that unsubstantiated reports are not mostly malicious or unfounded, but that they emanate from the same signals as verifiable, substantiated abuse.
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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 | 0.074 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.016 | 0.044 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| 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".