Hope or Despair Revisited: Assessing Progress and New Challenges in Global Fisheries
Bibliographic record
Abstract
ABSTRACT Marine fisheries are crucial to the economy, livelihood, food security and culture of coastal nations and communities, significantly contributing to the United Nations Sustainable Development Goals. A decade ago, T. J. Pitcher and W. W. L. Cheung highlighted the dichotomy in the perception of fisheries' status, concluding that long‐term sustainability and benefits to people were threatened by overexploitation, climate change, pollution, habitat change and other human stressors. They advocated for a fundamental shift towards ecosystem‐based management, better enforcement of existing regulations and more inclusive and equitable management practices. In this paper, we provide an updated review of the status of global fisheries, reflecting on policy actions, key assessments and research findings over the past decade. While there is a growing recognition of the need for sustainable fisheries management and ocean protection, the overall status of fisheries has not improved. Despite progress in international and national policies addressing direct and indirect drivers such as climate change and harmful practices, these trends have not been reversed. Many challenges identified by Pitcher and Cheung and others persist. Additionally, new and emerging issues such as deep‐sea mining, plastic pollution, unhealthy aquaculture development, increasing social inequity and the rapidly increasing push for the acceleration of the blue economy exacerbate the complexity of achieving fisheries and other ocean management goals. Debating whether there is more hope or despair in global fisheries has become irrelevant. Pathways to ‘bend the curve’ for fisheries are clear, and effective actions are now urgently needed to achieve desirable and sustainable fisheries.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".