Applicability of Microfinance for Adaptation to Sea Level Rise Impacts
Bibliographic record
Abstract
The aim of this study is to study the applicability of hypothetical microfinance for household adaptation to sea level rise impacts at community level. The study examines two hypothesis: 1) microfinance can (cannot) be applied as an adaptive measure to the impacts of sea level rise; 2) whether or not the factors of risk perceptions, attitudes, social references, microfinance conditions, government supports, and demographic influence an individual participation to a designed microfinance. The study sites are six vulnerable coastal villages located in the Gulf of Thailand. A designed microfinance for adaptation to sea level rise impacts is assumed in hypothetical market and tested with residents in the villages. Acceptance analysis, Pearson correlation, and stepwise regression analysis are used to test the hypothesis of the study.The study results reveal that microfinance can be applied for household adaptation to sea level rise impacts at community level. However, there are some correlated factors that affect individual participation to the designed microfinance. The likelihood of successful implementation of microfinance for the adaptation purposes is depended on how those factors affecting participation are properly addressed by implementer.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".