A Bottom-Up Understanding of Illegal, Unreported, and Unregulated Fishing in Lake Victoria
Bibliographic record
Abstract
Illegal, unreported, and unregulated (IUU) fishing is a major concern in fisheries management around the world. Several measures have been taken to address the problem. In Lake Victoria, the alleviation of IUU fishing is implemented through the Regional Plan of Action (RPOA-IUU), which restricts use of certain fishing gear, as well as prohibits fishing in closed areas and during closed seasons. Despite the long-term efforts to monitor and control what goes on in the fisheries, IUU fishing has persisted in Lake Victoria. Inspired by interactive governance theory, this paper argues that the persistence of IUU fishing could be due to different images that stakeholders have about the situation, rather than the lack of management competency. Through structured interviews with 150 fisheries stakeholders on Ijinga Island in the southeastern part of Lake Victoria, Tanzania, using paired comparison questionnaires, the study elicits stakeholders’ perspective about the severity of different locally-pertinent fishing-related activities. The results show that while fisheries stakeholder groups agree on their judgments about certain fishing gears, some differences are also apparent. For instance, fisheries managers and scientists do not always agree with fishing people about what activities cause the most damage to fisheries resources and ecosystem. Further, they tend to consider some IUU fishing-related activities less damaging than some non-IUU fishing. Such disparity creates governability challenges, pointing to the need to revisit relevant regulatory measures and to make them consistent with the knowledge and judgments of all stakeholders. Based on these findings, we discuss governing interventions that may contribute to addressing IUU fishing in Lake Victoria and elsewhere.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".