Overcoming obstacles for disaster prevention: Challenges and best practices from the EU and beyond (Deliverable 2.2)
Bibliographic record
Abstract
This report is an analysis of the findings and insights drawn from six national reports, as well as a European Union (EU) and global report developed as part of the ESPREssO project ('Enhancing Synergies for Disaster Prevention in the European Union').The analysis serves to highlight common themes and issues across EU countries, with relevant insights from the EU and global levels, concerning three central challenges for successful disaster management in the EU: (1) the integration between climate change adaptation (CCA) and disaster risk reduction (DRR); (2) bridging the gap between science and policy; and (3) strengthening transboundary crisis management in the EU.The purpose of the report is to provide input and insights into the final deliverables in the ESPREssO project.Chapter 3 explores the obstacles and ways forward for the integration between climate change adaptation (CCA) and disaster risk reduction (DRR) in legislation, policies and institutional arrangements.The following issues were identified: weak horizontal and vertical coordination in CCA and DRR governance; lack of capacities of local governments for implementation of CCA and DRR strategies; resource limitations and poor implementation of strategies; lack of funding; political awareness and risk perception; inadequate platforms for stakeholder communication and engagement; unequal attention paid to CCA and DRR; and, conflicting priorities between disaster response and risk reduction.Chapter 4 addresses the problems and potentials for bridging the gap between science and policy for DRR and CCA, in order to strengthen policy-making, the quality and availability of risk assessments, as well as public awareness of hazards, risks and vulnerabilities.The following issues were identified: inadequate platforms and structures for bringing science closer to policy, and the need to build platforms; demand for risk expertise in public institutions; a lack of available risk data on vulnerability; limited scope and outlook of research; low public awareness of disaster risks and climate change impacts; complex scientific terminology; and, new media landscapes.Finally, Chapter 5 concerns the barriers and opportunities for strengthening transboundary crisis management in the EU, looking at existing agreements and arrangements between individual countries regionally, and at the EU level generally.The following issues were identified: isolated national thinking and lack of political will; absence of policies and tools for transboundary crisis management; lack of standardized forms of communication; international cooperation across national government levels; a lack of attention to CCA as a cross-border issue; and, conflicting priorities in environmental resources and DRR. Keywords: disaster risk reduction
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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 | 0.066 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".