Socioeconomic Development of the Local Community and Causes of Land Use Change in Belaga, Sarawak, Malaysia
Bibliographic record
Abstract
The state of Sarawak in Malaysia has witnessed a surge in demand for its valuable land resources, primarily driven by major commodity investors establishing plantations. These land concessions have further exacerbated land scarcity and the depletion of crucial forest areas, which are essential for the livelihoods of indigenous communities. The indigenous community in Sarawak, heavily dependent on forests, faces significant challenges due to land use changes. This study aims to identify the drivers of land use change and assess the socioeconomic impact on the local community in Murum, Belaga, Sarawak. The research was conducted in December 2020, utilizing a combination of quantitative and qualitative approaches. Questionnaires, in-depth interviews, and site observations were employed to collect data, involving 511 household heads and ten key informants. Statistical analysis using SPSS software was performed on the collected data. The findings highlight that the primary cause of land use change in the study area was the hydroelectric dam project, followed by the establishment of oil palm plantations, logging activities, forest plantation development, government policies, and agricultural activities. Regarding infrastructure satisfaction levels, respondents ranked sport/recreational facilities as the most important, followed by electricity supply, house/accommodation facilities, education facilities, clean water sources, communication accessibility, health facilities, and road accessibility. Overall, the data indicates an improvement in the economic and social conditions of families due to land use changes in the area. However, the study concludes that there is still a need for further improvements in essential services, including providing multiple clean water sources to each household, enhancing access to healthcare services, and improving road conditions. Improved road access will foster increased business and social activity, facilitating job opportunities and contributing to poverty alleviation within the local community.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".