COMMUNITY PARTICIPATORY SUSTAINABLE LAND MANAGEMENT BYELAW FORMULATION IN THE HIGHLANDS OF CENTRAL ETHIOPIA
Bibliographic record
Abstract
Widespread adoption of sustainable land management (SLM) innovations by land users is considered key in addressing the rampant land degradation in the high rainfall and densely populated highlands of eastern and southern Africa. However, absence of enabling policy environments hamperes massive adoption of SLM innovations among rural communities. This paper presents the process and outcomes of a participatory approach for formulating and implementing SLM byelaws in the central highlands of Ethiopia. The participatory approach utilised three complementary tools, namely, stakeholder analysis, community needs assessment and policy dialogues. The stakeholder analysis revealed that several government institutions, non-government organisations (NOGs) and community groups promote SLM practices. Poor coordination among actors, top-down approach in planning and implementation, and limited capacity of communities hamperes SLM scaling up efforts. Stakeholder engagements culminates in establishing innovation platforms (IPs) at district and watershed levels tasked with coordinating SLM scaling up efforts. While the community needs assessment identified and prioritised SLM issues that needed to be resolved, the policy dialogue engaging IPs formulated three SLM byelaws and mechanisms for implementation.
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.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".