Resilience principles and a leverage points perspective for sustainable woody vegetation management in a social-ecological system of southwestern Ethiopia
Bibliographic record
Abstract
Addressing ecosystem destruction and unsustainable development requires appropriate frameworks to comprehensively investigate social-ecological systems. Focusing on woody plant management in southwestern Ethiopia, we combined social-ecological resilience and a leverage points perspective to (1) assess how stakeholders perceive and operationalize resilience principles; (2) investigate resilience challenges and solutions across different levels of systemic depth; and (3) assess how different stakeholder groups noted challenges and solutions at different levels of system depth. Data were collected in focus group discussions with multiple types of stakeholders and analyzed via quantitative content and descriptive analysis. All stakeholder groups identified two principles currently applied in the landscape, while other principles were not currently applied widely. In total, we identified 37 challenges and 44 solutions to resilience, mainly focused on “deeper” systemic change. This trend was noted across stakeholder groups, but particularly by local people. Based on our work, we suggest to foster bottom-up changes in system goals, rules, paradigms, and intent, drawing explicitly on local people and their knowledge. More broadly, we suggest that further research on combining social-ecological resilience and leverage points perspectives could be helpful to better navigate and transform social-ecological systems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| 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".