Bibliographic record
Abstract
In July of 1997, Dallas area child protection activists appealed to local police and media broadcasters to launch the nation's first Amber Alert system, a crime prevention program enabling law enforcement agencies to activate regional emergency broadcast systems to announce missing children alerts. From these origins, the Amber Alert system evolved into one of the most successful interstate innovation campaigns in recent history. With strong support from child-protection and victim's-rights advocates, every state in the union adopted the Amber plan between 1999 and 2005. The Amber Alert proved to be such an appealing response to kidnapping that identical versions of the child protection law were soon adopted internationally. Between 2002 and 2004, every Canadian province adopted the Amber program. In 2006, the United Kingdom launched its own version of the Amber plan called the Child Rescue Alert. Although the Amber Alert was exceptional in the sheer speed and scope of its implementation, such abrupt patterns of policy adoption are far from unique in American politics. The reenactment of the death penalty, prohibition, term limits, tax revolts, state auto lemon laws, English Only language legislation, “three strikes” sentencing guidelines, mandatory child auto-restraint requirements, and sex-offender registries stand as prominent examples of policy innovations that moved rapidly and extensively throughout the nation. Most of these innovations were championed by well-organized interest groups, and appealed broadly to voters across the states. In many cases, the innovation was adopted by more than 30 states in fewer than six years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".