Perceived Self-Efficacy and Adaptation to Climate Change in Coastal Cambodia
Bibliographic record
Abstract
In response to climate change at different spatial scales, adaptation has become one of the focal points of current research and policy developments. In the context of coastal Cambodia, there is little research on local level adaptation to climate change. Using ordinal logistic and logistic regression analyses, this study examines the relationship between perceived self-efficacy and anticipatory and reactive adaptation to climate change among 1823 households in coastal communities in Cambodia. Findings indicate that individuals who reported higher categories of self-efficacy were more likely to report both anticipatory (OR = 1.74, p < 0.001) and reactive adaptation (OR = 3.61, p < 0.001) measures. Similary, tndividuals who had higher education had higher odds of reporting anticipatory adaptation (OR = 1.71, p < 0.001) and reactive adaptation (OR = 1.63, p < 0.05) when compared with those without formal education. Participants who have been living in their current residence for six years or more were more likely to report anticipatory adaptation (OR = 1.09, p < 0.05) and reactive adapation (OR = 1.22, p < 0.001) compared with those who had lived there for a shorter duration of time. Region of residence was positively associated with both anticipatory and reactive adaptation. In this context, it is important to note that individuals in the most agriculture-dependent and climate sensitive province reported the least anticipatory and reactive adaptation measures. Policy makers should target empowerment of the most vulnerable population to facilitate better adaptation behavior, and mainstreaming of knowledge on climate change adaptation through both formal and informal education at the community level.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".