Social Capital and Well-Being of Small-Scale Fishers in the West Coast Island of Peninsular Malaysia
Bibliographic record
Abstract
Poverty in small-scale fisheries is a global issue; most of the time, the solution to poverty is discussed through economic variables. Scholars highlight the contribution of social capital factors to the livelihoods of small-scale fishing communities and suggest that social aspects can be used as an alternative solution to reduce poverty. The concept of social capital has been extensively used to explain the relationship between social capital factors and wellbeing. The role of social capital in the well-being of small-scale fisher communities in Malaysia needs to be clarified. The current study investigates the contribution of various livelihood assets to the household income of small-scale fishers in Malaysia. Data for the survey were collected from 182 respondents from across multiple fishing villages on Langkawi Island, off the west coast of Peninsular Malaysia, using a structured questionnaire. The partial least square (PLS) technique was applied for statistical analysis. The study’s empirical findings depict that social capital, trust, job experience, and financial capital are important factors contributing to fishers' household income. The results show that the contribution of social capital and trust factors are significant to household income, indicating that social factors are essential in improving the well-being of small-scale fisher households in Malaysia. Policy for livelihood improvement of small-scale fisheries in Malaysia needs to prioritise investments in financial, human, natural and physical capital assets.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".