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Record W1548585674 · doi:10.1111/conl.12085

Mass Media Influence and the Regulation of Illegal Practices in the Seafood Market

2014· article· en· W1548585674 on OpenAlexaff
Stefano Mariani, Jamie Ellis, A. N. o'Reilly, Amanda L. Bréchon, Carlotta Sacchi, Dana Miller

Bibliographic record

VenueConservation Letters · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundHigher Education Authority
KeywordsBusinessEnforcementMass mediaProduction (economics)Product (mathematics)Compliance (psychology)AdvertisingMarketingEconomicsPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

Abstract Following media exposure on the issue of seafood mislabeling in Ireland, results of repeated forensic testing of cod product labeling suggest that media attention played an important role in determining significant improvements in the supermarket retail sector, but had no detectable effect on “take‐away” food services. Differences in the chains of production and in compliance requirements to European labeling laws may explain the divergent responses of the two sectors. The findings from this study indicate that it may be possible for mass media to occupy an influential role in fisheries and environmental management and policy, provided that (i) primary research findings are correctly reported by popular media and (ii) governmental agencies follow up the short‐term reaction to media exposure with appropriate enforcement measures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.005
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations48
Published2014
Admission routes1
Has abstractyes

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