Social capital and adaptation to wildfire in southern Greece
Bibliographic record
Abstract
Wildfires are exacerbated by climate change and are a growing concern globally. Adaptation to climate change impacts including increased wildfires requires action at individual, community, and government levels. In this paper, we use the concept of social capital, or the norms and networks that enable collective action, as a lens to understand wildfire adaptation. Specifically, we assess the roles of different types of social capital in fire risk mitigation compared to fire response in southern Greece, which has experienced numerous large wildfires in recent years. To this end, we conducted 33 interviews with 44 rural residents and policy actors. Our findings illuminate a complex relationship between social capital and fire adaptation. We found that overall, social capital, a critical variable in environmental hazard adaptation, has been eroded by depopulation of rural areas and government management characterized by shifts in leadership and a culture of clientelism (or political quid-pro-quo). Bonding social capital influences villagers’ participation in volunteer fire brigades. However, bonding social capital is not sufficient to support proactive preparation for increasingly severe wildfires. Linking social capital, specifically between villagers and government officials, is often undermined by lack of trust and the failure of outsiders to utilize local knowledge. Our findings build on other cases in environmental hazards adaptation, where proactive preparation is often neglected in comparison to response. Additionally, our research adds to understandings of how low trust in state actors affects social capital in the face of environmental hazards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".