Towards "Good" Native Land Governance
Bibliographic record
Abstract
Sarawak is the largest state in Malaysia, where two-thirds of the population are Indigenous. This study aims to evaluate, through the lens of good governance principles, the current practice of the Sarawak State’s formal land governance of lands associated with Native Customary Rights (hereafter known as Native land governance). Being quantitative in nature, this study conceptualises an evaluation framework for good governance principles as applied to Native land governance. Next, this study empirically tests out the framework by adopting a multi-criteria decision-making tool known as The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS analysis enables the integration of perceptions between State/private groups and Indigenous groups. The output of the TOPSIS analysis is summarised in a strength, weakness, opportunity, and threat (SWOT) format according to the TOPSIS closeness value. Unfortunately, results show that the weaknesses outnumber the strengths in Sarawak’s Native land governance. Among these issues, Indigenous respondents highlight major issues with the Sarawak land registry’s efficiency in delivering outcomes that are equitable for Indigenous land rights. This study ends with recommendations on how the state of Sarawak can move towards compliance with good governance principles in relation to lands associated with Native Customary Rights.
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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 | 0.009 | 0.008 |
| 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 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".