Social connectivity and adaptive capacity strategies in large-scale fisheries
Bibliographic record
Abstract
Large-scale fisheries are important social-ecological systems that are increasingly being threatened by global climate change.Adaptive capacity is key for moving fisheries onto climate resilient pathways, however, implementing policies to improve adaptive capacity is challenging given the many diverse stakeholders involved in fisheries.Previous research suggests social networks are integral to adaptive capacity because social connectivity can enable, or constrain, knowledge and information sharing.We examine the network of communication among stakeholders in the Basque tropical tuna freezer purse seine fishery in the eastern Atlantic Ocean.We use cluster analysis, descriptive statistics, and exponential random graph models to assess whether different types of actors, occupying different network positions, value similar adaptive capacity strategies.The results indicate that many actor types are frequently connected within the fishery.Preferences for adaptive capacity strategies vary within and across actor types, and the preferences of highly central actors are generally more homogeneous and narrowly focused.All actors agree on the importance of the social organization domain from adaptive capacity, while fishing industry representatives tend to have the most holistic perspective on adaptive capacity overall.We discuss the implications of these findings as they relate to policies for supporting adaptive capacity and climate resilient 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".