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Funding Priorities: Big Barriers to Small‐Scale Fisheries

2008· article· en· W2078136060 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueConservation Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaPew Charitable Trusts
KeywordsScale (ratio)FisheryGeographyBusinessEnvironmental resource managementEnvironmental planningEnvironmental scienceBiologyCartography

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.062
GPT teacher head0.260
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it