Science-Policy Interface for Disaster Risk Management in India: Toward an Enabling Environment
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
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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