Funding Priorities: Big Barriers to Small‐Scale Fisheries
Bibliographic record
Abstract
Since the mid-1990s there has been a concerted effort to encourage fisheries sustainability by targeting large-scale, high-catch fisheries and by raising consumer awareness. Because of the often slow pace of regulatory approaches, this voluntary, market-oriented effort has been structured so as to avoid government involvement. But have smallscale fisheries, our best option for sustainable use of fisheries resources, been lost in the market-based push toward sustainability? In financial terms the largest sustainable fisheries initiative has been the U.S.-based Seafood Choices campaign, largely funded by the Packard Foundation. From 1999 to 2004, Seafood Choices invested $37 million in more than 30 nonprofit organizations to promote marketbased sustainable seafood initiatives, such as ecolabeling certification and seafood wallet cards that tell consumers which fish are being caught sustainably (Bridgespan Group 2005). In contrast, over the last decade, only 2 U.S.-based nonprofit organizations have invested <$1.5 million in research and policy reform related to global fisheries subsidies. Since the late 1990s the World Wildlife Fund (WWF) has had one full-time person working on fisheries subsidies and lobbying countries to reduce subsidies (approximate cost < $100,000/year). In 2005 the nonprofit organization Oceana began a campaign against fisheries subsidies with some staff working part-time on the issue of subsidies (approximate cost < $75,000). In 2006 Oceana ramped up their efforts against subsidies (approximate cost $125,000– 150,000) and in 2007 spent approximately $400,000 on subsidy-related efforts, including a paid advertising campaign, media, staff, and travel (M. Hirshfield, personal communication). Although they are often described as very variable between countries, small-scale fisheries are characterized as fishers operating in boats of 15 m or less, or without
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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 | 0.047 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".