Sociological Perspective and Legal Protection of Customary Land: Solution to Determination of Traditional Forest in Indonesia
Bibliographic record
Abstract
This study is motivated by the perspective of government officials and the mechanism for determining customary forests which is often the cause of conflict between indigenous peoples and plantation entrepreneurs who accept concessions from the government. The aims of this study are to evaluate the legal positivism perspective that continued domein verklaring during the Dutch colonialism saw customary forests as state forests as long as they had not been determined by the government so that they could be concessioned to plantation entrepreneurs. The results showed that in order to prevent conflicts and to simplify and speed up the mechanism for determining customary forests, an idea is offered to apply the legal processing (rechtsverwerking) analogy in customary law that has been accepted by the national land law. The physical control of customary forests by customary communities by collecting products, utilizing and conserving the forest, regulated in customary law and not denied by indigenous peoples, who border, is sufficient as a sign that the customary forest is under the control of the customary community concerned. Likewise, for the stipulation mechanism, it is necessary to give authority to the local government to be able to determine customary forests.
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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 | 0.005 | 0.022 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".