Adaptive Transition Management for Transformations to Agricultural Sustainability in the Karnali Mountains of Nepal
Bibliographic record
Abstract
Current agroecological approaches to farming have provided a limited understanding of transformations to sustainability, particularly in subsistence agrarian economies of geographically isolated regions of the world. Some suggest mitigating social and ecological impacts of modern industrial farming while others advocate for local adaptation to changes in socioecological systems, such as climate change, extreme weather events, and biodiversity loss. This article investigates effective pathways of fundamental transformations in technologies, livelihoods, and lifestyles referred to as “agricultural sustainability transitions” in the Karnali Mountains, the most impoverished region of Nepal. Findings suggest that neither management of change referred to as transition management nor adaptation to change referred to as adaptive management effectively leads to agricultural sustainability transitions in this region of the country. An integration of these two approaches, which this article theorizes as “adaptive transition management,” can help charter transition pathways through system innovation making new and improved technologies more accessible and adaptable to smallholders while developing local capacity to adapt to changes in agroecological 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".