Steps to sustainable forestry success: a case study analysis of community-based forest management in developing countries
Bibliographic record
Abstract
Community-based forest management (CBFM) is an approach that involves small-scale community owned and managed forestry that is drawing serious attention for its applicability to achieving sustainable forest practices. In this paper, I research the question: What factors explain the success and failure of CBFM in developing countries? Thirty-four case studies of CBFM in 14 developing countries were reviewed and statistically analyzed. A total of 47 independent factors were found to significantly influence the outcome (success or failure) of these CBFM experiments, of which the most important determinants of success were: the comprehensiveness and objectives of the management plan, land tenure, ownership and property rights, types of support, participation (in particular that of women), perceptions (project confidence, perceived tangible benefits, social capitol, environmental concern, and equality between community members), agricultural and land management systems used (use of agroforestry techniques, rehabilitation of degraded lands), national community based forest management policy, community governance and law, socio-economic attributes, and the degree of decentralization. These factors were then used to develop steps to sustainable forestry success as a guide to the initiation and development of successful community based forest management in developing countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".