Role of Governance in Developing Disaster Resiliency and Its Impact on Economic Sustainability
Bibliographic record
Abstract
This study explores the role played by governance in developing disaster resiliency and its impact on economic sustainability in Greece. Descriptive research was undertaken, and data were collected from 180 local governance leaders in Western Macedonia, Greece, to gain a deeper understanding of the role of governance in developing disaster resiliency and economic sustainability. The study confirmed the hypothesis that the focus of governance in developing disaster resiliency positively affects economic sustainability. The ability of governance to develop disaster resiliency and economic sustainability is mostly through leadership, engaging civil society, and international cooperation. These roles played by governance are also influenced by different political, economic, cultural, and social aspects, which all have an impact on the risk governance systems that cut across levels of resource assurance, technical support, and disaster risk management. Governance may have a significant impact on the overall design of rules and systems, including legislation, different decision-making procedures, and policy-implementation mechanisms, via political leadership. In terms of economics, the primary responsibility of governance is to support disaster risk-reduction systems. Governance must encourage risk awareness on a national basis through intensive disaster risk research, technological development, disaster-reduction education, and emergency response skills practice.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".