Bibliographic record
Abstract
This timely book provides the inside story of the development of mobile public alert and warning technology in the United States and addresses similar systems being used in Australia, Canada, Japan, and the Netherlands. This book provides a comprehensive account of how mobile-smartphone systems are transforming the practice of public alert and warning in the United States. Recent events have vaulted mobile alert and warning technology to the forefront of public debates concerning the hazards of the digital age. False alarms of ballistic missile attacks on Hawaii and Japan, the non-use of mobile alerts during the Northern California wildfires, and the role this technology plays in supporting police manhunts and counterterrorism efforts have prompted reconsideration of how these systems are used. Drawing upon interviews with officials, executives, experts, and citizens, the book provides an in-depth analysis of the events and contexts influencing the trajectory of mobile public alert and warning and charts a course for its improvement. The book first introduces readers to the high stakes involved in the transformation of public alert and warning, explaining how new research is revealing the benefits, limitations, and risks of mobile technology in the disaster communication context. Three case studies then illustrate issues of risk, trust, and appropriateness in mobile public alert and warning.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".