Social and Economic Impacts of Community Managed Reforestation and Natural Regeneration of Forestry Development, the Case of Humbo District, Ethiopia
Bibliographic record
Abstract
This study was undertaken in Humbo District, Ethiopia. The objective of this study is to assess the effects of reforestation initiatives on the socio-economy of the rural households brought by the existence of the Community Managed Natural Regeneration (CMNR) project. Four Kebeles (administration units in a district) were selected for the study, based on their geographical location. One Kebele Administration (KAs) from four corners (North, South, East and West) of the closure area was selected. A total of 203 households were involved in the survey. Data were generated through questionnaire, FGDs, KIIs, and physical observation. The study depicted that respondent households were participating in the designation process and subsequent management of the Humbo CMNR project which in fact is considered to be good indicator for its sustainability. They also have good perception on planting trees, reforestation programs and on the ownership of forests as well. It was noted that in some of the surveyed KAs, drought, water constraints and strong wind are the main challenges of the communities. There are available institutional setups and bylaws to manage and protect the forest which was formulated by many stakeholders including local communities. To mention some; bylaws workable for penalizing illegal intrusion into the closure, igniting fire on the forest and benefit sharing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".