Sufficient and Sustainable Livelihood via Community Economy: Case of Natural Farming Program in East Malaysia
Bibliographic record
Abstract
Community economy is an alternative mean for sustainable livelihood emphasized under the Sustainable Livelihood Approach (SLA) and Sufficient Economy Approach (SEA). Both approaches support the participatory development strategies which concern on the empowerment of marginalized people through an efficient utilization of local resources. The purpose of this article is to analyze the outcomes of the community-based economic activity executed through the natural farming programme in selected rural areas of East Malaysia. Our finding indicates that most participants have gained positive economic and social outcomes from the programme. The programme provides economic benefits such as reduction of household food expenditures, productive usage of household idle resources, increase of household income and saving. In addition, the programme also contributes to social benefits such as provide fresh vegetables, promote closeness among family members, increase self-satisfaction and happiness, increase closeness among community members, healthier feeling and increase agricultural skills and knowledge. The mean of each item show consistent trend that ranges between 3.68 (lowest) to 4.34 (highest). Therefore, the community economy conducted through the natural farming programme has benefited the rural area people in line with the idea of SLA and SEA.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".