Bibliographic record
Abstract
Crisis Management (Esmaeili, 2009) offers an early and comprehensive framework that positions crisis management as a fundamentally communicative, governance-driven, and sustainability-dependent process. Drawing from eleven chapters, the work integrates organizational communication, strategic decision-making, ecological resilience, and critical-infrastructure coordination to explain how societies prepare for, absorb, and recover from crises. The book emphasizes that crises are rarely caused solely by natural hazards; rather, they emerge when communication networks fail, information systems break down, or centralized structures delay vital decisions. Esmaeili advances a model where decentralization, transparent communication, and community-based preparedness significantly enhance resilience. The text highlights the importance of geospatial intelligence (GIS), cross-agency collaboration, and culturally responsive public messaging, anticipating contemporary crisis-informatics research. Later chapters explore interdependencies among water, electricity, gas, telecommunications, and transportation systems, demonstrating how infrastructure failure becomes a communication failure. The book concludes by proposing an integrated crisis-governance model aligning strategic communication, sustainability principles, and distributed decision-making. Although originally produced for a governmental organization, this conceptual synthesis provides a valuable foundation for scholars in crisis communication, public administration, emergency management, organizational behavior, and resilience studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.036 |
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".