A picture of change: using photovoice to explore social and environmental change in coastal communities on the Andaman Coast of Thailand
Bibliographic record
Abstract
Coastal communities experience a wide array of environmental and social changes to which they must constantly adapt. Further, a community's perception of change and risk has significant implications for a community's willingness and ability to adapt to both current and future changes. As part of a larger study focusing on the adaptive capacity of communities on the Andaman Coast of Thailand, we used Photovoice to open a dialogue with communities about changes in the marine environment and in coastal communities. This article presents the results of two exploratory Photovoice processes and discusses prospects for using the Photovoice method for exploring social and environmental change. Changes examined included a number of broader environmental and social trends as well as ecological specifics and social particularities in each site. Participants also explored the social implications of environmental changes, the impacts of macro-scale processes on local outcomes, and emotive and active responses of individuals and communities to change. Photovoice is deemed a powerful method for: examining social, environmental, and socio-ecological change, triangulating to confirm the results of other scientific methods, revealing novel ecological interactions, and providing input into community processes focusing on natural resource management, community development, and 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".