Increasing industry involvement in international tuna fishery negotiations
Bibliographic record
Abstract
The private sector can play a prominent role in global ocean governance. Yet, industry stakeholders are diverse, and how specific companies engage with policymakers remains poorly understood. Here, we focus on Western and Central Pacific tuna fisheries, which provide ∼60% of global tuna catch and a critical source of income for developing island states. We identified relationships between companies and governments in international fishery negotiations from 2005 to 2018. Relative industry attendance nearly doubled during this time, and 15 of 158 companies have dominated corporate representation since 2014. Further, industry members outnumbered government policymakers on half of the ten largest delegations, and 70% of island state delegations included foreign companies. Meeting attendees corroborated the influence of industry stakeholders, but this differed across countries. During our study period, management of tuna fisheries improved overall, suggesting that company involvement does not hinder sustainability outcomes and could play a supportive role when agendas are aligned.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".