Land Rights and Agrarian Reform in Forest Areas: A Basis for Sustainable Development
Bibliographic record
Abstract
Land use changes from forest to non-forest typically precede or follow human expansion in areas with forests.As the granting of legal land ownership is not allowed in forest areas, this raised issues about the justice of land access for people living in forest areas.This essay aims to investigate the procedure for enhancing community-owned and used forest areas' land ownership rights.The research was conducted in two villages in Bengkayang Regency, West Kalimantan, utilizing content analysis to discern community land use variations in forest areas.These variations may inform the allocation of land rights to the community and considerations related to forest area release to the community.The findings reveal that the complexity of land use and ownership in forest areas has given rise to conflict and disturbances, which are exacerbated by a lack of clarity in land administration and the legal system.In an effort to address this issue, the government has promulgated Presidential Regulation 88 of 2017 to govern the establishment of a Team for the Acceleration of Settlement of Land Tenure in Forest Areas (PPTKH), and Presidential Regulation 62 of 2023 to expedite the implementation of agrarian reform.However, this program faces various challenges, both from a juridical, geographical, socio-economic, and institutional perspective.One solution that can be implemented is to provide land ownership rights in forest areas, including land suitable for agriculture, residential areas, fishponds, social facilities, and farming on dry land.The use of these lands can be used as a basis for granting land ownership rights to the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".