Crisis-ready telecom: Global approaches to emergency management in telecommunications
Bibliographic record
Abstract
This paper examines the integration of Emergency Management (EM) frameworks into telecommunications regulation to address climate-driven disasters. EM principles—prevention, preparedness, response, and recovery—offer a structured approach to strengthen telecom networks and manage crises. By analyzing international practices, the study identifies critical gaps in funding, coordination, and regulatory alignment, highlighting opportunities to align telecom policy with EM planning. The findings provide actionable recommendations to foster cross-sector collaboration, promote regulatory flexibility, and enhance infrastructure resilience in an increasingly interconnected and disaster-prone world. • Bridging Telecom Policy and EM Planning: There is an opportunity to integrate two distinct yet complementary domains: telecom policy and emergency management (EM)planning. These fields have historically evolved in silos, but integrating their frameworks will imporve network resilience. • Power of EM Frameworks : EM principles—prevention, preparedness, response, and recovery—provide a systematic foundation for embedding resilience into telecom policy and practice. • Lessons from International Best Practices: The U.S., Japan, and EU demonstrate how EM-driven strategies—such as partnerships, targeted investments, and integrated policies—can aeffectively address telecom vulnerabilities. • Gaps in Funding and Coordination: Critical gaps remain in proactive funding, unified EM adoption, and cross-jurisdictional collaboration. • Evaluating Traditional Telecom Policies : Traditional telecom policies must be critically evaluated for their impact on resilience.incentivizingfacilities-based competition, technological diversity, and robust network deployment, particularly in underserved areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".