Linkages between Social-learning Networks and Farm Sustainability for Smallholder Shrimp Farmers in Sri Lanka
Bibliographic record
Abstract
Shrimp farming has the potential to improve income and diversify livelihoods in rural Sri Lanka.The industry faces challenges including low productivity, disease outbreaks, and unsustainable practices.Shrimp farmers' perceptions about access to knowledge and their knowledge-exchange social networks were examined.A cross-sectional survey of 225 farmers was completed in two separate shrimp farming regions.The questionnaire assessed social learning networks, farm-level sustainability, demographics, and wealth of farmers.Associations between the number of connections in social learning networks (degrees) and the other factors from the questionnaire were examined using Poisson regression analysis.Overall, social learning networks were not highly connected (median farmer degree =2) and network structure varied by geographic location and farmer ethnicity.Higher social learning network degrees were associated with increased wealth and decreased ecological sustainability; however, this varied by ethnicity.Significant differences in networks between geographic areas and ethnicities point to the need for contextually adapted knowledge mobilisation activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".