Assessing Accommodation Suppliers’ Perceptions of Climate Change Adaptation Actions on Koh Phi Phi Island, Thailand
Bibliographic record
Abstract
Koh Phi Phi Don is among the most visited island tourism destinations in Thailand. Due to the island’s topography and development patterns, most accommodation suppliers on the island are likely to be exposed to a range of climate change impacts, particularly sea-level rise, which can pose a severe risk to the local tourism operations. This study aimed to explore perceptions of climate change adaptation actions in response to impacts typically associated with climate change. This study, furthermore, investigated possible obstacles, barriers, and incentives influencing decision-making processes of accommodation owner-managers (the private sector) to adapt to climate change. The investigation builds on 81 surveys and 12 in-depth interviews. The findings provide evidence that most of the sampled businesses already implemented (consciously or not) climate change adaptation measures, such as insurance coverage, water treatment appliances, and staff training on emergency responses. Through a concentration of power on the island, their action is hindered, which creates a barrier to a sustainable and climate risk-informed development pathway.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".