Water Resilience in the Indian Context: Definitions, Policies, Approaches and Gaps
Bibliographic record
Abstract
India has been facing multiple water-related challenges owing to a large population. Increase in water crisis, water resources-related pollution, mismanagement of existing resources, and an imbalance in water policies due to various gaps and lacuna at both State and Central level of governance. Water resilience is emerging as a research field that addresses multiple water management issues responding to emerging challenges, such as global climate and environmental changes. The study focuses on secondary data and literature studies from Web of Science and Scopus databases to examine the concepts of resilience as defined by literature, dimensions with planning and governance and its implications in the existing Indian water policy framework. The methodology incorporated the systematic Delphi technique in formulating the governance gaps in the research area. The highlighted gaps are further ranked using statistical methods. According to the findings, the most critical gap is the lack of integrated strategic policy planning encompassing all water-related disruptions. As its identified gaps are interconnected and aggravate each other, a comprehensive approach is required. The study suggests potential research areas that strengthen water resilience governance. There is a need to increase resilience, signifying the sheer urgency in embellishing resilience to the increasing demands and effective management of existing water resources.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".