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Record W1837194521 · doi:10.3389/fmars.2015.00072

The influence of the Sustainable Seafood Movement in the US and UK capture fisheries supply chain and fisheries governance

2015· article· en· W1837194521 on OpenAlexfundno aff
Alexis Gutiérrez, Siân Morgan

Bibliographic record

VenueFrontiers in Marine Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationUniversity of British Columbia
KeywordsLegitimacyCorporate governanceSustainabilitySupply chainBusinessTraceabilityGovernment (linguistics)Position (finance)Environmental governanceSustainable developmentFisheryEnvironmental resource managementPolitical scienceEconomicsMarketingEcologyPoliticsFinance

Abstract

fetched live from OpenAlex

Over the last decade, a diverse coalition of actors has come together to develop and promote sustainability initiatives ranging from seafood eco-labels, seafood guides, traceability schemes, and sourcing policies in Western seafood supply chains. Based on a literature review, we trace the development of the Sustainable Seafood Movement, which has been working to reform sustainability practices in the seafood supply chain. Focusing on the US and the UK capture fisheries, we explore the roles of key actors and analyze the dynamics within and between actor groups through a conceptual model derived from semi-structured interviews. We argue that the Sustainable Seafood Movement is different from previous social movements in that, in addition to actors advocating for government reform, it has motivated supply chain actors to participate in non-state market driven governance regime. The movement and its actors have leveraged their legitimacy and authority garnered within the supply chain to increase their legitimacy and authority in public governance processes. As the movement continues to evolve, it will need to address several emerging issues to maintain its position of legitimacy and authority in both the supply chain and public governance processes.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2015
Admission routes1
Has abstractyes

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