Science-Policy Interface for Disaster Risk Management in India: Toward an Enabling Environment
Bibliographic record
Abstract
The 2013 Uttarakhand floods highlighted the enormous challenges faced by disaster risk management organizations and actors who had to deal with it on a real-time basis. Unusual and extreme rainfalls accompanied by a series of cloudbursts triggered the flooding. In recent times there has been a significant increase in the quantum of scientific research on such weather- and climate-related extremes in some of the most vulnerable regions in India. Although the role of science and research has been adequately recognized and included in India's national development policies and programmes, including the Disaster Management Policy (2009), integration of this accumulating scientific and research evidence into disaster management policies, planning, and practices in the country has been limited. Uttarakhand floods were followed by Cyclone Phailin (2013), and the untimely hailstorms in central India (March 2014). The resulting challenges for the country and its policy makers are complex and gigantic. It is under these emerging circumstances of complexities that the urgency for proactive and effective science-policy interface is discussed. Building on the existing institutional and policy opportunities in India, an enabling environment to facilitate such science- policy interface for disaster risk management is suggested. We discuss collaboration, co-production, coherence, and continuity as some of the organizing principles of this enabling environment.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".