Improving Community-level Governance: Adaptive Learning and Action in Community Forest User Groups in Nepal
Bibliographic record
Abstract
It is widely believed that Participatory Monitoring and Evaluation (PM&E) can help community organisations improve their internal learning and governance. However, the processes of programme monitoring and evaluation as practised by many organisations lack the elements of community participation and ownership and the appreciation of its contribution to community learning. Wider lessons on participatory development demonstrate that only locallyinitiated and community?led monitoring can improve communities' performance and change their institutional practices. Drawing on the recent experience of Livelihoods and Forestry Programme (LFP), this paper makes the case for community?generated planning, selfmonitoring and evaluation for adaptive learning and good governance in Community Forest User Groups (CFUGs) in Nepal. These processes, conceptualised as Adaptive Learning and Action (ALA), have enabled CFUGs to identify their vision, formulate activities to achieve the vision, and regularly monitor the progress against the identified indicators. The process has also enhanced transparency, participation and accountability in CFUG governance. Full text is available at the ForestAction websiteDOI: http://dx.doi.org/10.3126/jfl.v8i2.2309 Journal of Forest and Livelihood 8(2) February 2009 pp.67-77
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 | 0.003 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".