From Crisis to Community: Exploring Informal Leadership and Rural Women in Community Resilience and Community-Based Adaptation to Climate Change
Bibliographic record
Abstract
As climate change continues to threaten the most vulnerable communities, strengthening community resilience and engaging in adaptation methods is critically needed. Developing countries and rural communities are often most susceptible to climatic impacts, and with the increased frequency of natural disasters and unpredictable weather causing concern for agricultural practices and exacerbation of inequalities, community resilience and community-based adaptation plans are of the utmost importance. Informal leaders are members of communities that hold no formal position within institutions or policy-making decisions, yet they hold great responsibility in the success of community capacity and strengthening of social capital. Rural women, more specifically, bring unique experiences and contextualized knowledge that contribute to creating sustainable community-based adaptation methods within rural communities. This research aims to better understand rural women in developing countries and their role as informal leaders for community-based adaptation and responses to global climate change. This paper serves as an introduction to how informal leadership and social capital drives the success of community-based adaptation to climate change within rural communities in developing countries, with an emphasis on women-focused experience and contextualized knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".