Pathways of household adaptiveness to climate risk: A survey in a semi-arid region of Indonesia
Bibliographic record
Abstract
Communities in semi-arid regions, including farmers and livestock holders, are more likely to suffer from environmental stresses due to climate change volatility. Therefore, understanding the diverse pathways to community resilience and adaptability is imperative. This study explores the pathways of household adaptiveness to climate risks on Sumba Island, a semi-arid region in Eastern Indonesia characterised by smallholder livestock farming and frequent climate-induced disasters. It investigates the strategies employed by rural households to cope with climate-related events and defines household adaptability as the capacity to prepare for and respond to shocks through actions such as accumulating savings and diversifying livelihood portfolios in anticipation of climate crises. The research hypothesises that prior disaster experience, place attachment, social capital, participation, and social protection are key predictors of household adaptiveness. Using structural equation modelling analysis conducted with SmartPLS software, the study analyses data collected from a survey of 300 households across ten villages located in coastal, inland, and suburban areas of East Sumba. Findings suggest that prior disaster experience and social protection are stronger predictors than other variables, serving as direct pathways to household adaptiveness to climate change risks. The study implies that effective adaptive social protection policies can foster community engagement and enhance household adaptability and resilience in disaster-prone regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".