Smallholder farmer resilience to water scarcity
Bibliographic record
Abstract
Abstract Water scarcity poses one of the most prominent threats to the well-being of smallholder farmers around the world. We studied the association between rural livelihood capitals (natural, human, social, financial, and physical) and resilience to water scarcity. Resilience was denoted by farmers’ self-reported capacity to have avoided, or adapted to, water scarcity. Proxies for livelihood capitals were collected from two-hundred farmers in South Sulawesi, Indonesia, and their associations with a typology denoting water scarcity impacts analyzed with a Taylor-linearized multinomial response model. Physical and natural assets in the form of irrigation infrastructure and direct access to water sources were saliently associated with overall resilience (avoidance and adaptation) to water scarcity. Years of farming experience as a form of human capital asset was also strongly associated with resilience to water scarcity. Factors solely associated with the capacity to adapt to water scarcity were more nuanced with social capital assets showing closer associations. A household with a larger number of farm laborers had a higher likelihood of being unable to withstand water scarcity, but this relationship was reversed among those who managed larger farming areas. We discuss possible mechanisms that could have contributed to resilience, and how public policy could support smallholder farmers cope with water scarcity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".