Who is King of Sarawak’s Rainforest? An insight to Sarawak’s land corruption led by its Chief Minister and his family
Bibliographic record
Abstract
Malaysia’s 13th General Elections were hopes of many to be the turning point of change, breaking Barisan Nasional’s (BN or National Front) 56 years of governance. BN in recent years had been plagued with allegations of corruption and cronyism. Land grabs in the state of Sarawak, exposed an intricate and systematic corruption that happens in all levels of government in Malaysia. The perils of the rainforest in Sarawak are uncovered through a corrupt systematic mass deforestation through the governance of its Chief Minister Taib Mahmud. Was Malaysia’s latest election successful in dethroning Taib and his family out of their political powers? Taib holding several portfolios puts him in immense political and economic power. For more than 30 years, Taib has made use of his various ministerial roles to methodically harvest the state’s natural resources and amassing a personal fortune of USD $15 billion. The first family of Sarawak too has their share in Taib’s fortunes. Kickbacks, corrupt land deals, evasion of Malaysian tax and the service economy of corruption were true and evident in the family’s dealings. Taib’s eldest daughter, Jamilah Taib and her husband Sean Murray, well known socialites in Ottawa, Canada play a major role in the slow death of Sarawak’s rainforest and indigenous tribes. One woman, Clare Rewcastle Brown who manages Sarawak Report and Radio Free Sarawak is determined to bring down the supreme rule of Taib and his family. Her media outlets aim “… to provide that platform and to offer an alternative vision of justice, transparency and a fairer future in Sarawak.”
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".