Determinants of Livelihood Strategies Among Rubber Smallholders: Case Study in Kedah Malaysia
Bibliographic record
Abstract
This study analyzes the determinants of alternative strategies undertaken by rubber smallholders in the state of Kedah. This study used primary data obtained through a survey of 343 smallholders using structured questionnaires in four districts. The information collected covers the demographic profiles and components of livelihood assets. Data were analyzed using one-way ANOVA and Chi-Square descriptive tests while inferential statistics were analyzed using Multinomial logit to identify determinants of strategy selection. The results showed that a majority of 44.9 percent of rubber smallholders opted for rubber and other agricultural activities while only 9.6 percent choose to use a combination of rubber and non-agricultural activities as their alternative strategies. The size of family dependence, duration of experience in agriculture, household income, technology, land size, assistance sources, gender, information sources, involvement in social associations and societies and money savings are all factors that contribute to the selection of smallholder alternative strategies. It is hoped that the government can focus on smallholder awareness measures in an effort to increase their involvement in alternative activities. Agricultural and non-agricultural activities are seen to improve the adaptive capacity of smallholders and thus increase their income.
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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) | 0.000 | 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 | 0.001 |
| 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".