Co-production of knowledge and strategies to support climate resilient fisheries
Bibliographic record
Abstract
Abstract Knowledge co-production offers a promising approach to design effective and equitable pathways to reach development goals. Fisheries Strategies for Changing Oceans and Resilient Ecosystems by 2030 (FishSCORE), a United Nations Ocean Decade programme, will co-produce knowledge that advances solutions for climate resilient fisheries through networks and partnerships that include scientists, stakeholders, practitioners, managers, and policy experts. FishSCORE will establish (1) a global network that will develop broadly relevant information and tools to assess and operationalize climate resilience in marine fisheries and (2) local and regional partnerships that will apply those tools to identify and forward context-specific resilience strategies. FishSCORE's activities will be guided by a set of core principles that include commitments to inclusivity, equity, co-leadership, co-ownership, and reciprocity. FishSCORE will focus on identifying solutions for climate resilient fisheries, and it will also advance goals associated with capacity, power, and agency that will support iterative, pluralistic approaches to decision-making in fisheries experiencing ongoing climate-driven changes. This process of co-producing knowledge and strategies requires considerable investments of time from all partners, which is well aligned with the Ocean Decade. However, secure funding must be prioritized to support and implement co-production activities over this long time horizon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".