Analysis of Catch and Fishermen Family Welfare in West Sumatra Province: Simultaneous Equation Approach
Bibliographic record
Abstract
This research is motivated by the catch and the fishermen family welfare in West Sumatra Province is not yet optimal, so this study aims to analyze the factors that affect them.Furthermore, the novelty of this research is to carry out an elaboration of studies on fishermen households by focusing on the analysis of catches and the fishermen family welfare being investigated within a simultaneous equation framework.The population in this study were fishing households in West Sumatra Province which had 498 fishing gears.The sampling in this study used the cluster method, which a total sample of 373 respondents.The important finding in this study is the catch will increase if it is driven by the fishermen family welfare, the type of catch, fishermen productivity and fishermen socio-cultural environment.Furthermore, the fishermen family welfare will improve if it is driven by catch, fishermen socio-cultural environment, selling price of fish and fishermen family happiness.The recommendation from this study is that the government needs to facilitate increasing catches and fishermen family welfare through fishermen insurance programs, institutions, funding and business diversification training based on the bottom up concept so that fishermen have access and stronger bargaining power.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".