Guided transformations for communities facing social and ecological change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it