INNOVATION PLATFORMS: A TOOL FOR SCALING UP SUSTAINABLE LAND MANAGEMENT INNOVATIONS IN THE HIGHLANDS OF EASTERN UGANDA
Bibliographic record
Abstract
Sustainable Land Management (SLM) technologies for preventing land degradation have been pilot tested in highlands of eastern Uganda with success and are available for uptake by farmers in the zone. Despite the available technologies and successful pilot experiments, the effect and uptake of the SLM innovations still remains insignificant. This has been attributed to lack of incentives, innovative institutional governance structures and policy processes to accelerate uptake and utilisation of SLM technologies. Innovation systems approach was experimented in scaling up SLM innovations in the highlands of Eastern Uganda. Stakeholders were organised into platforms and empowered to promote SLM practices in the landscape. Members of IPs selected the SLM innovations and implemented them with support from National Agricultural Research Organisation (NARO). More households adopted SLM practices including trenches, contour bunds and agroforestry. Twenty three tree nurseries were established and over 350,000 tree seedlings distributed for planting. The platforms facilitated collective visioning, sharing of skills and knowledge and strengthened participation of local governments in research and promotion of SLM technologies. When well initiated and operationalised, innovation platforms are effective avenues for scaling up adoption of SLM innovations to a wider landscape and communities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".