Two faces of shrimp aquaculture: commonising vs. decommonising effects of a wicked driver
Bibliographic record
Abstract
Much coastal fisheries literature supports the idea that shrimp aquaculture has the potential to cause considerable social and environmental destruction. The aim of the paper is to highlight the two faces of shrimp aquaculture as a wicked driver, emphasizing its potential role in activating systematic conversion of lagoon –based fisheries commons to non-commons and vice versa. We use the cases of aquaculture-led privatisation in Chilika Lagoon, located in the Bay of Bengal area of India, and collective action surrounding shrimp aquaculture in Northwestern Sri Lanka. For both studies, data are collected through mixed research methods, including semi-directive interviews, focus group discussions, and participant observations. Our analysis shows clear evidence that shrimp aquaculture can potentially contribute to either making commons or losing commons depending on the context and influences of multi-level drivers. Aquaculture-led factors contributing to the process of losing commons in Chilika are: large-scale, individually owned aquaculture operations; encroachment of customary fishery commons; loss of commons rights (access and entitlements); breakdown of commons institutions; policy changes; caste politics and resource conflicts; ecological disturbances; change in fishing practices. In Sri Lanka, aquaculture related factors contributing to making commons are: coordinating discharge; built-in incentive for stewardship; multi-level commons institutions; collective decision-making; bottom-up management approach; mixed commons regime; and small-scale operations.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".