Seeking clarity on transparency in fisheries governance and management
Bibliographic record
Abstract
Calls for greater transparency in fisheries are becoming increasingly common. They promise better evaluation of management interventions, more participatory decision-making, effective monitoring and surveillance, and sustainable and equitable exploitation of shared resources. However, there is often a lack of clarity regarding what is meant by ‘transparency’ and how it is best achieved. This term is often used interchangeably, by both decision-makers and civil society, and sometimes inappropriately when a more specific term may be called for. This can lead to the propagation of sweeping assumptions and reductive views on what fisheries transparency is, what it can achieve, what the costs and benefits will be, and to whom. As such, attempts to realize the benefits that transparency promises for fisheries often struggle once they are systematically applied from concept through legislation to practice. This paper explores the various manifestations of transparency in the context of fisheries governance and management as well as its limitations. Subsequently, to provide clarity and broaden the transparency-related discourse, a simple framework is presented of the multifaceted nature of transparency in fisheries. This exploration is crucial because, when harnessed correctly, transparency has the potential to support a more sustainable and equitable future. However, realizing this potential requires deeper appreciation of the intricacies of transparency beyond surface-level interpretations.
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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 | 0.108 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".