Addressing water-related shocks and coping decision through enhanced community participation: case studies from Ganga basin, Uttarakhand, India
Bibliographic record
Abstract
Abstract Farming communities in the Upper Ganga basin, nestled in the Himalayan region, are finding it extremely difficult to face water-related shocks, which stand to profoundly impact their quality of life and livelihoods, due to climate change. Often, coping strategies (technological or institutional interventions), developed by planners, become counter-productive as they are not in cognizance with the end user community. This study presents a methodology to enable incorporation of community knowledge and expectations in planning by integrating participatory rural appraisal (PRA) with geographic information systems, leading to better informed coping strategies. As part of this, we create thematic maps which: (i) capture information on a spatial scale (otherwise lost during PRA), (ii) facilitate community participation for further research and planning in their contexts, and, (iii) co-create knowledge to develop a shared understanding of water-related hazards at the village level. The proposed methodology is presented through three case study sites – two in the plains (<500 masl) and one in the middle (500–1,500 masl) elevation regions of Upper Ganga basin. We show how this way of approaching context analysis facilitates community involvement as well as co-creating a knowledge base which can help researchers and government officials with mindful planning of interventions in the area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".