Beyond Local Case Studies in Political Ecology: Spatializing Agricultural Water Infrastructure in Maharashtra Using a Critical, Multimethods, and Multiscalar Approach
Bibliographic record
Abstract
Political ecologists (PEs) have powerfully illuminated dynamics responsible for the uneven distribution of resources and risk in society. However, localized PE approaches have been criticized as insufficient for producing careful generalizations needed to affect policymaking. We offer an approach to critically explore factors that shape the distribution of climate adaptation interventions—and their potential equity and sustainability-related implications—across larger, policy-relevant scales. Our methodology uses local field-work findings to inform secondary data collection and specify mesoscale regression models, which reanalyze, at larger spatial scales, potentially meaningful relationships between social, economic, and environmental factors and the distribution of adaptation initiatives. An epistemological heuristic is offered to navigate the consistencies and inconsistencies between local qualitative and mesoscale quantitative data to develop a more comprehensive, yet partial, understanding of scaled political–ecological relations. The integrative approach is applied to analyze how sociospatial and biophysical characteristics affect the distribution of more than 16,000 farm ponds across 352 subdistricts in Maharashtra, an emerging adaptation subsidized by the state government to reduce crop risks from precipitation variability. The degree of compatibility between local qualitative and regional-scale quantitative results can support the development of novel research questions and actionable science for policy change.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".