Commentary – Child maltreatment surveillance: enumeration, monitoring, evaluation and insight
Bibliographic record
Abstract
Application of epidemiologic surveillance to child abuse and neglect (CAN) presents specific challenges related to varying definitions and incident reporting. Definitions of abuse and neglect differ within and across countries, obscuring estimates of the true magnitude of the problem. Definitions also vary depending on the nature of the child protection system. Countries may lack legal or social systems with specific responsibility for responding to and recording reports of CAN, particularly countries where populations are remote or in flux (e.g. due to conflict). Underreporting of CAN results in underestimates of prevalence. Violence by caregivers toward children is often known only to the perpetrator, and depending on the developmental capacity of the child, the victim. Further, CAN cases may be reported to a wide variety of sentinels (e.g. educators, clergy, physicians, law enforcement, child welfare), or may not be reported to any official source at all. Social stigma and unintended consequences of reporting, as well as cultural and political barriers, also impact reporting both within communities and globally.
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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 | 0.026 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.079 | 0.062 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".