Understanding Socio-Ecological Vulnerability to Climatic Change through a Trajectories of Change Approach: A Case Study from an Indigenous Community in Panama
Bibliographic record
Abstract
Abstract This paper identifies and characterizes vulnerability to climatic change in the Ngöbe-Buglé Indigenous community of Playitas, Panama, using a “trajectories of change” approach. Playitas is a community composed of swidden forest farmers that is undergoing rapid rates of change as a result of demographic shifts, regional development, and climate change. Working in collaboration with a community organization, various methods were used to identify and characterize livelihoods, social-ecological dynamics, environmental change, and behavioral responses to change, with the aim of informing future planning in the community. Qualitative methods included semistructured interviews (n = 26), community workshops, and participant observation. Causal-loop diagrams based on field data and the perceptions of community members were created to model trajectories of change. The research reveals that change is driven by both internal and external factors and that the responses of community members create both reinforcing and balancing feedback loops that overall generate increased stress in agricultural systems, social structures, and environmental components. Although community members historically relied on social relationships, Indigenous knowledge, and remoteness as sources of resilience to external disturbances, climate change is acting as a “multiplier” of their existing vulnerabilities and is undermining their capacity to adapt to current and future climatic changes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".