Fisheries Centre research reports. Volume 31, number 4
Bibliographic record
Abstract
This research analyses the nature of human dependence on bottom trawling as a necessary precursor to constraining its impact. Bottom trawling involves dragging a weighted net along the seabed, catching and/or damaging every organism in its path. Bottom trawling can negatively affect fishers, resulting in fewer opportunities for artisanal and small-scale fishers, diminished food security, increased human rights violations, and social and violent conflicts. This study focuses on India, where trawling started in 1956 and where 79% of total landings by trawls that targeted shrimp were already gathering unintended catches by 1979. While the obvious solution seems to be to limit bottom trawling, decision-makers are often challenged by conflicting economic, social, and environmental imperatives. The objective of the study was to understand fishers' motivations to start, stay in and stop bottom trawling to better address challenges faced by decision-makers. A mix of peer-reviewed and grey literature was used to conduct a systematic literature review. The results show that the fishery and fishers chose to begin bottom trawling and stay in the industry primarily because of offers of subsidies, potentially better income, and likely profits. The bottom trawl industry persisted despite declining resources because of (i) the industries' capacity to exert power which allowed them to extend a sunset business, and (ii) poor enforcement of regulations that should have constrained trawling. We underline the entrenched nature of the trawl industry, where fishers were trapped in bottom trawling because of accumulated debt to people higher in the trade. Fishers stopped trawling when constrained by regulations, resource depletion, and low financial returns. Fishers' motivations to participate in bottom trawling varied according to their role in the trawl industry, with owners often reaping more benefits from trawling than the crew. Recognizing 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.309 | 0.142 |
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".