MEKANISME AKSES PADA HAK KEPEMILIKAN DI KESATUAN PENGELOLAAN HUTAN PRODUKSI MERANTI, SUMATERA SELATAN
Bibliographic record
Abstract
The interest of various parties on forest utilization access lead to the ambiguity of property rights due to user overlapping.This research explained the ambiguity factors of property rights from access mechanism and its relation to the land conflict.The research using purposive sampling method to obtain data of land use change, documents, historical study, as well as in-depth interviews of 123 people key informant.Rapid Land Tenure Assessment (RaTA) and descriptive analysis method were used to analyze the data.The results showed that both access and property rights theory could explain the overlapping use on forest area in Meranti Forest Management Unit (FMU).Analysis of rights-based access mechanism explained factors within the property rights status and the causes of land overlapping, i.e. the dynamics of management change, boundaries area issues, and lack of control.The factors of land user based on structure mechanism were the kinship ties, patroness system or pesirah, community and religious leaders.The access of structure mechanism have lead to claim of 38.53% areas of Meranti FMU.Changes of the rules have increased new users and causing overlapping between bussines license holder with community access.The research recommends avoiding change of area management forms, and for involving local communities in determining new users, duration, and profit 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".