Determining Factors Influencing Livelihood Strategies Decisions of Coastal Fishermen in Pulau Pangkor, Malaysia
Bibliographic record
Abstract
This article examines factors influencing varying decisions made on livelihood strategies amongst coastal fishermen in Pulau Pangkor, in the state of Perak in Peninsular Malaysia. Two livelihood strategies - Livelihood Intensification and Livelihood Diversification - were chosen as the key dependent variables. The seven independent variables were social demographic, trend of income, coping strategies, risk associated with fishing activities, willingness to venture, willingness to learn and sustainable income. Since there were more than one dependent variables, and using a newly-developed framework, Structural Equation Model (SEM) was used to assess the relationship between each independent variable and dependent variables. The findings of this research demonstrate that (1) education level, level of income versus expenses, trend of output, coping strategies adopted, risk associated with fishing activities, and sustainable income are significant factors affecting fishermen’s choice of livelihood intensification strategies, while (2) education level, level of income versus expenses, trend of output, and willingness to learn are significant factors affecting their choice of livelihood diversification strategies. The findings then were used to build the Livelihood Strategies Determinant Framework (LSDF), which is more appropriate for the coastal fishermen of Pulau Pangkor.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".