Risk Perception of Small Islands Community on Climate Change: Evidence From Mepar and Baran Islands, Indonesia
Bibliographic record
Abstract
This study explores climate risk perception of communities in two small islands, Mepar and Baran, located in Lingga Regency, Riau Islands Province to fill in the lack of knowledge regarding the topic in Indonesia and to support island bottom-up climate change adaptation planning. This study uses proportional random sampling and a questionnaire survey of 165 households to collect data related to demography, level of knowledge, level of risk perception, and adaptation actions taken by communities. We use descriptive statistics and employ discriminant analysis to determine factors influencing risk perception of these small islands’ communities. We identify two categories of risk perception in this study as a basis for analysis, namely risk perception on climate change hazards and climate change risk perception on community’s life. This study finds four factors that consistently influence both types of risk perception on climate change. These are the number of climate change indicators perceived, age, and the experience on extreme weather both at sea and on the island. Other influencing factors which have a partial role include the duration of residence on the island, place of birth, education level, and trade relations. We then critically discuss the results within the complexity of small island development and bottom-up climate change adaptation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".