Comparison of Land Law Systems: A Study on Compensation Arrangements and Reappraisal of Land Acquisition for Public Interest between Indonesia and Malaysia
Bibliographic record
Abstract
Land is a livelihood land for everyone to achieve prosperity in various fields; besides that, the land is also the essential capital in the development of a nation, and its benefits must be exploited as well as possible. There are still many lands affected by the procurement of development for the public interest that are detrimental to the community, including compensation and land that an appraisal has not appraised. This research method uses normative juridical research with the approach of laws and concepts and collects primary legal materials in existing regulations in both countries. Data collection techniques consist of literature study, observation, interviews, and use of questionnaires. The results of this study found that both Indonesia and Malaysia regulate compensation arrangements in the Act. Although both of them depart from different legal systems, where Indonesia is subject to the civil law system and Malaysia is subject to the common law system, both have the same. In Indonesia, it has not explicitly regulated reappraisal in its law, in the future, it is necessary to consider the pattern of reappraisal carried out by Malaysia, because this pattern according to the author, with the current legal vacuum, it is necessary to have a fast track process to provide justice and legal certainty.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".