The Influence of Socio-Demographic Factors in Adopting Good Aquaculture Practices: Case of Aquaculture Farmers in Malaysia
Why this work is in the frame
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Bibliographic record
Abstract
<p>This study examined the influence of socio-demographic characteristics on the level of Good Aquaculture Practices (GAqP) among aquaculture farmer in the Northern part of Peninsular Malaysia. Primary data was obtained from survey that was conducted on 121 brackishwater and freshwater pond aquaculture farmer in the states of Kedah and Penang. Descriptive analysis was applied to identify the socio-demographic characteristics of aquaculture farmer and their level of GAqP. Multiple Linear Regression model was used to analyze the relationship between socio-demographic factors and the level of GAqP. The findings has revealed that the level of GAqP among brackishwater pond farmer is satisfactory where almost 84 per cent of farmer practicing GAqP at the level of 60 per cent and above with the mean value of 71.9 per cent. While the mean level of GAqP for freshwater pond farmer was at 50.3 per cent with only 18.6 percent of them practicing GAqP at the level of 60 per cent and above. Age and having technical knowledge related to aquaculture were the main factors that significantly influence to the level of GAqP among aquaculture farmer. Therefore measures related to the enhancement of technical knowledge among aquaculture farmer should be deliberated in the formulation of aquaculture development programs to ensure the sustainable development of aquaculture in Malaysia.</p>
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it