Evaluating the roles and reach of philanthropic foundations in sustainability efforts for tuna
Bibliographic record
Abstract
Abstract Tuna fisheries provide over 5 million tonnes of seafood annually to the global market but have historically raised conservation concerns due to weak management measures and impacts on non‐target wildlife. The focus of the first environmental awareness campaigns in seafood focused on dolphin bycatch in tuna fisheries in the 1980s. Since then, the sustainable seafood movement has evolved considerably, with philanthropic foundations playing a key role as agenda‐setters and funders of work carried out by non‐governmental organizations (NGOs). Here, we used tuna as a case study and investigated how three US foundations and associated NGOs have affected tuna fisheries reform through two primary pathways: advocacy for improved fishery management at intergovernmental meetings, and engagement with fishing companies in fishery improvement projects (FIPs). We found a total of USD 28.65 million was allocated to tuna‐related work from 2013 to 2021. While each foundation had different funding profiles, 65% of all grant funds were directed to two key priority areas: market leverage and RFMO advocacy. Further, almost 60% of all funding was allocated to only three NGOs, all of which are central actors at RFMO meetings, and which are collectively engaged in over 85% of all tuna FIPs (by volume). We reflect on how this concentrated funding relates to the overarching sustainable seafood agenda of these foundations and provide recommendations to ensure financial support and objectives remain transparent and do not perpetuate inequities between tuna fishing countries.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".