Assessment of Village and Community Forest Sustainability: Evidence from the Local Level
Bibliographic record
Abstract
The implementation of social forestry, particularly at the local level, must ensure ecological, economic, and social sustainability. The sustainability level assessment from various Social Forestry of Perhutanan Sosial (PS) schemes is crucial to recognize, evaluate, and improve its implementation at the local level. Therefore, this study aims to assess the sustainability level and identify the lever indicators of the sustainability of Village Forest or Hutan Desa (HD) and Community Forest or Hutan Kemasyarakatan (HKm) management as the two largest schemes of PS. The Rapid Appraisal for Village and Community Forest (RapVCF) with Multidimensional Scaling (MDS) approach was developed to assess the sustainability of the three HD and HKm cases. The results revealed that HKm SB had the highest sustainability value compared to the three HD and two other HKm. HKm SB is considered relatively sustainable, with a sustainability value above 50 in ecological, economic, and social dimensions. In general, economic and social dimensions have a lower sustainability value compared to the ecological dimension. Some indicators play a pivotal role to the sustainability level of HD and HKm, namely conditions and changes in forest cover, the manageable area, market coverage, income for forest management, claims/mastery of working areas, and benefit distribution mechanisms. Evaluation and improvement of these indicators must be prioritized to increase the sustainability level of HD and HKm.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".