Determinants of Improving the Welfare of Fishermen's Households in the Coastal Areas of West Sumatera
Bibliographic record
Abstract
This study aims to model the welfare of fishermen's households in the coastal area of West Sumatra which is determined by the socio-cultural environment, catches and added value of 373 fishing households with fishing gear by using Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The contribution of this research lies in the concept of the socio-cultural environment in improving the welfare of fishermen's households through the catch and added value that has been ignored by previous researchers. The results of the study found that the fisherman household welfare model can be achieved through strengthening added value. This study suggests to the government to increase the added value of fishery products through the added value obtained from the difference between the selling price and the cost of materials or other supplies from fishing activities, fish farming, fish handling, fish processing, and distribution in a production process. Furthermore, this study suggests to further researchers to involve the study of the physical environment such as natural conditions as a determinant of the welfare of fishermen's households.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".