Biodiversity and social benefits in community-based forest management: the Leuser ecosystem
Bibliographic record
Abstract
This thesis examines the ability of Community Based Forest Management (CBFM) systems to serve the dual function of maintaining biodiversity while providing benefits to local communities. It examines the relationship between biodiversity and social benefit in a variety of forest zones in the Manggamat Community Conservation Forest in South Aceh, Indonesia. The Manggamat Community Forest is the flagship CBFM initiative in the Leuser Ecosystem, an area of global biodiversity significance. The CBFM system in Manggamat is guided by Adat, a traditional set of laws derived by the community in order to manage resources from the forest. The research utilized a formative, results-based evaluative approach, and indicates that CBFM has the potential to balance the protection of keystone and other significant species with the ability to provide benefit at a level equal to, if not superior, other rural areas in South Aceh and the province of D.I. Aceh. Although the system is intended to distribute benefits in a equitable manner, there is some disparity in the distribution of income through unsustainable harvesting of timber for sale by a small minority of resource users. While this puts the sustainability of the CBFM system at risk, it is a problem that is addressed in the research, and can likely be resolved through negotiation at the community level using Adat in representing greater community interests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".