Devolution: a mechanism for scaling adoption of Sustainable Land Management in eastern Africa highlands.
Bibliographic record
Abstract
Sustainable Land Management (SLM) technologies are known to improve food production and productivity in areas prone to high soil degradation, arising from water and soil nutrient losses. In eastern Africa, mechanisms for mitigation of this land degradation have been developed, but their uptake has been minimal. Devolution, a mechanism known to entrust communities with decision making tools and powers to plan, implement and monitor activities was tested in the highlands of eastern Africa. Generation of consensus on how to implement the scaling of adoption of SLM innovations is a crucial aspect for evaluating the devolution process. Assignment of clear roles and responsibilities facilitated involvement of multi-displinary stakeholders in managing the process of scaling sustainable land management innovations. At district level, officials appreciated the intervention, streamlined activities in their work plans leading to increased budgets for natural resource management which resulted into increased adoption of SLM technologies. Farmers were able to express their land management needs and give direction to operations through priotising interventions (trenches, contour bunds and agroforestry) key to their area and facilitated dissemination of SLM technologies. Key Words: Decision support tools, land degradation RÉSUMÉ
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".