Understanding the Drivers Behind Why People Start, Stay in, and Stop Bottom Trawling: Insights for Policymakers
Bibliographic record
Abstract
Our research analyses people's motivations in the bottom trawling industry as a crucial step toward mitigating its impacts. Bottom trawling, which involves dragging a weighted net across the seabed, captures and damages virtually all organisms in its path. This practice has negatively impacted fishers by diminishing food security, intensifying competition for artisanal and small-scale fishers, escalating human rights violations, and triggering social and violent conflicts. While the obvious solution seems to be to limit bottom trawling, decision-makers are often challenged by conflicting economic, social, and environmental imperatives.Focusing on India, Thailand, Vietnam, and Cambodia, where bottom trawling is prominent, we investigated why trawling started, stayed, and stopped, providing insights to support decision-makers. We conducted a novel systematic literature review incorporating both peer-reviewed and grey literature sources to capture these insights. Few articles directly examined fishers’ motivations in the bottom trawl industry; instead, relevant details were scattered across studies, often as brief mentions within broader topics. These snippets contributed to our dataset.Our analysis indicates that the fishery started bottom trawling primarily because of economic factors, such as the provision of subsidies and the introduction of technology. The industry stayed with trawling for economic and social reasons, including subsidies, the demand for bottom trawl products, and illegal fishing. The trawl industry only stopped trawling when forced by social pressures, such as the effective implementation of fisheries regulations.Our review reveals ambiguity about the scale of trawling, unclear terminology for trawl gear, and a lack of clarity about the actors involved in the industry. All are critical challenges when crafting policy. Managing a fishery often involves managing people, which requires understanding human behaviour influenced by intrinsic and extrinsic factors. Recognising and addressing these results will be crucial in determining how best to constrain bottom trawling effectively.
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".