Guided transformations for communities facing social and ecological change
Bibliographic record
Abstract
Communities and their surrounding landscapes are intricately interconnected. This is evident in the Intermountain West of the United States of America, where large cities sit within vast landscapes otherwise containing small rural communities with farm, forest, and rangeland. Climate change and other stresses increase the tensions along the gradient of urban to rural communities and landscapes, and theoretical frameworks are needed to conceptualize regime shifts within these social-ecological systems. We propose a framework called Guided Transformation (GT) that translates new knowledge into action by incorporating diverse perspectives and values that prioritize community and environmental well-being. Guided Transformation combines elements from social, ecological, and technological systems (SETS) theory, resilience theory, and sustainability transitions research. In this manuscript, we outline the GT framework and its relationship to related theory and literature, and we then provide three case studies that demonstrate the application of the GT framework. The first case study is in the upper Rio Grande watershed in New Mexico, where innovative governance strategies are addressing the challenge of wildfire and watershed protection. The second is in eastern Washington and the Yakima Basin, where drought drove innovation in the form of an integrated water management plan that is now helping to meet the needs of both farmers and fish in the basin. In the final case study, we discuss work on the Navajo Nation addressing food, energy, and water security and Indigenous sovereignty through solar greenhouse technology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".