Modeling the Adoption of Aquaculture Technologies among the Members of 4-H Club Youth
Bibliographic record
Abstract
Aquaculture farming in the country Philippines has played a vital role concerning employment and food security for every Filipino. This article aimed to look into a piece of information that explicate the factors of aquaculture technology adoption among the 4-H club youth members. The study uses primary data gathered from selected members of 4-H club youth in Southern Leyte, Philippines. Standard descriptive measures were calculated to characterize and describe the collected information and a statistical model were engaged to capture the significant predictors of the adoption of aquaculture technologies among youth. Results revealed that there are only a few (10.17%) of the youth members are adopting aquaculture technology. The members are neutral on their perception of aquaculture technology concerning complexity, economically viable, and environmentally safe. In addition, they disagree on the compatibility and minimal risk of the said technology. The regression model reveals that older adults (p-value<0.1), males (p-value<0.1), and higher income (p-value<0.05) are more likely to adopt the technology. The inverse effect from 4-H coordinator influence (p-value<0.05) and environmental safety characteristics (p-value<0.1) was found in the model regarding adopting the technology. Moreover, youth members are likely to adopt the technology if it is economically viable (p-value<0.05) to them. Hence, it is concluded that if the technology is affordable and understood by the farmers, there is a strong likelihood that they will adopt it in their respective places. The study suggests that the local government must support and implement more training and workshop for aquaculture technology to encourage and educate more youth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".