KEPASTIAN HUKUM DAN PENGAKUAN PARA PIHAK HASIL PENGUKUHAN KAWASAN HUTAN NEGARA DI PROVINSI RIAU
Bibliographic record
Abstract
Legal certainty and legitimacy of forest area can be gained through the gazettment process of forest area that starts from the designation, boundary demarcation, mapping, and ends up with the establishment. In Riau Province, these processes are stagnant, and, therefore, the legal certainty and legitimacy is difficult to achieve. What is really happenned is something that needs to be answered in this study. By using the analysis of strategy typology and descriptive qualitative analysis, this study has found that the gazettment issues of forest area consisted of three aspects, namely: designation, boundary demarcation and establishment. Social conflict has been accumulated along the gazettment process, so that the legal certainty did not lead to legitimacy. This problem happened due to: claims avoidance (PTB) to avoid failure in boundaries determination; policy narrative of the boundaries are not informed to community; inconsistency between the objective of boundary demarcation with the implementation; domination of all informed knowledge and information (BPKH); stages of gazettment were done just to fulfill administrative procedure; BPKH tasks issues; and state forest area regarded as the common pool resources (CPRs). This result proves that the improvement of government, policy in gazettment of forest area is seriously required.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".