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Record W2465516513 · doi:10.18278/wfp.2.2.3.1.3

Developing Local Sustainable Seafood Markets: A Thai Example

2016· article· en· W2465516513 on OpenAlexafffund
Courtney Kehoe, Melissa Marschke, Wichitta Uttamamunee, Jawanit Kittitornkool, Peter Vandergeest

Bibliographic record

VenueWorld Food Policy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsYork UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityBusinessScale (ratio)CertificationMarketingGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

Increasingawareness of degradation in ocean ecologies and fisheries has made seafood a leading edge in the green marketing movement, with most major buyers in the global North committing to buying seafood that has been certified as sustainable. But what about the significant and growing Asian markets, where seafood has become a healthy and prestigious food choice among wealthier consumers? Is it possible to develop a market for sustainably produced seafood among Asian consumers motivated by civil and ecological concerns? To address these questions our research traces how a Thai Fisherfolk Shop, located 4 hours to the south of Bangkok, has worked to develop an alternative market for seafood caught by local, small‐scale fishers. Although we find that there is a mismatch between the volume of aquatic species that fishers catch, the ability of the Shop to process, store, and sell seafood, and consumer demand, our analysis suggests that it is possible to create a market for small‐scale, sustainably sourced seafood in Thailand.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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 designTheoretical or conceptual
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

Citations5
Published2016
Admission routes2
Has abstractyes

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