Community Use Zone (CUZ) Model and Its Outcome in Malaysia Case Study from Crocker Range Park, Sabah
Bibliographic record
Abstract
The Sabah Parks has been experimenting with a new approach to forest governance, namely Community Use Zone (CUZ), in order to safeguard the forests from ongoing degradation while simultaneously providing opportunities for the affected communities to improve their living conditions and livelihoods. Despite the ongoing discourse to expand this approach, there is limited systematic study to understand the changes in governance as well as linking it to forest and livelihood outcomes. By conducting structured and semi-structured questionnaire interview to CUZ and non-CUZ community members as well as Crocker Range Park staffs, this study clarifies the changes in involvement of local people in rule making and implementation of diverse forest management activities and governance decisions as well as forest and livelihood outcomes from CUZ. The result showed that local people participation in rule making and implementation is enhanced after implementation of CUZ. While the forest outcome remains mixed, the CUZ has brought positive impact to the livelihood of the participants. CUZ also has positive externality to neighboring community in terms of their attitude towards the program.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".