Crisis-ready telecom: Global approaches to emergency management in telecommunications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it