The Role of FELDA and KESEDAR in the Development of Land in the District of Gua Musang: A Comparison the Socio-Economic Level of the Settlers
Bibliographic record
Abstract
The South Kelantan Development Authority (KESEDAR) and the Federal Land Development Authority (FELDA) are the two main agencies that develop land schemes in the district of Gua Musang, Kelantan. The nine land schemes developed by FELDA are Kemahang 3, Chiku 1, Chiku 2, Chiku 3, Chiku 5, Chiku 6, Chiku 7, Perasu, and Aring 1. KESEDAR also developed eleven land schemes namely Paloh 1, Paloh 2, Paloh 3, Chalil, Lebir, Meranto, Sungai Terah, Renok Baru, Jeram Tekoh, Limau Kasturi, and Sungai Asap. A large part of the schemes under the FELDA was planted with oil palm (84.7%) while the rest was planted with rubber trees. On the other hand, most of the land schemes under KESEDAR were planted with rubber (67%), while the remainder were planted with oil palm. The question that arises is to what extent is the role of both the agencies in advancing the standard of living of the settlers? What are the problems faced by the settlers and their implications regarding their socio-economic level? This paper will discuss the role played by KESEDAR and FELDA in advancing the standard of living of the settlers as well as identifying the problems faced by the settlers under the two agencies. The study found that many settlers earned between RM600 - RM1200 per month despite the efforts undertaken by FELDA and KESEDAR to improve the living standards of the settlers. The main problems faced by the settlers are: palm oil prices are volatile; oil palm trees are old, the old age of the settlers, and the settlers’ chidren migrating to the city.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".