Assessing differential vulnerability of communities in the agrarian context in two districts of Maharashtra, India
Bibliographic record
Abstract
Climate variability causes multiple difficulties to rural poor. The loss in agriculture production is the most predominant impact among many, especially in drought-prone regions of India. Aggravating this further are the non-climatic risks like depletion of groundwater, land fragmentation, lack of post-harvest structures and disappearing and deteriorating common property resources among many others. Within this context, the current study presents how agrarian livelihoods in rural Maharashtra has been transforming to adapt to both the changing climate and non-climatic drivers. A community engaging vulnerability assessment tool was used to explore the climate risks and vulnerabilities of different social groups. Insights indicate that vulnerability is socially differentiated and across farmer categories and social groups. Caste and social standing play a significant role in access to resources, land ownership, livelihoods choices and approaches – impacting their vulnerability to climate change. The study concludes that vulnerability assessments need to be conducted at lower scales, as climate risks vary even within small clusters of villages. This understanding helps designing programmes and policies that build adaptive capacities of rural poor and thus recommends integrating community engagement into academic research is critical.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".