Socio-Economic Drivers of Adoption of Small-Scale Aquaculture in Indonesia
Bibliographic record
Abstract
Aquaculture has a critical role in achieving the UN’s Sustainable Development Goals of increasing benefits that low-income and least-developed countries derive from marine resources. Its capacity to deliver these outcomes is challenging, particularly for marginalized groups. This is especially true if the introduction of novel technologies is applied with incomplete understanding of socio-economic and bio-physical contexts. We examined what socio-economic factors affect people’s perceptions of adoption of lobster aquaculture in rural households in Indonesia. We used multiple linear regression with model averaging to test the influence of five capital assets (human, social, natural, physical, and financial), including agency, equity, and household sensitivity, on people’s perceived ability to adopt lobster aquaculture. Agency and sensitivity had the greatest influence on the dependent variable. We then used correlation analysis to develop a heuristic model of potential indirect causal mechanisms affecting people’s perceptions of adoption. Our results point to the existence of a ‘sensitivity trap’, where more sensitive or marginalized households are less likely to engage in new economic opportunities. We emphasize the value of multifaceted programs for improving livelihoods, particularly for poorer, more vulnerable households as one way to support the UN’s commitment to using aquaculture as a pathway to achieving sustainable development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".