Simulating “Peripheral Harm” as an AMBER Alert Issuance Criterion: Implications for Anticipating Threats to Child Safety
Bibliographic record
Abstract
Although there is some limited research on the effectiveness of the America’s Missing: Broadcast Emergency Response (AMBER) Alert system, to date, there has been no research specifically examining the viability of prospective AMBER Alert issuance criteria. Using data acquired from various media accounts of 446 AMBER Alerts issued in the United States and Canada, we examine how well “peripheral harm” (harm to someone other than the abducted child during the course of the abduction) predicts subsequent harm to the abducted child. Counterintuitively (from the perspective of AMBER Alert issuance decision making), peripheral harm or threat is negatively associated with harm to the victim in cases involving an AMBER Alert. Furthermore, this negative finding is spurious, and is primarily driven by the fact that, disproportionately, the abductors who commit “peripheral harm” in AMBER alert cases are parents and other family members of the child who are presumably unlikely to harm child relatives despite whatever violence they might commit (or threaten) against others. We discuss the implications for the use of peripheral harm as an AMBER Alert issuance criterion, the empirical evaluation of the system, and the public discourse surrounding the AMBER Alert system and its relationship to child protection in general.
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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 | 0.028 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".