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Record W4414116748 · doi:10.1016/j.marpol.2025.106898

Beyond growth: Reshaping fisheries for a wellbeing economy

2025· article· en· W4414116748 on OpenAlexaff
Ingrid Kelling, Nathan Bennett, Kate Barclay, Andrew Jeffs, Cristina Pita, Birgitte Krogh-Poulsen, Tobias Troll, Evgenia Micha, Julia Cirne Lima Weston, Iain Black, Ibrahim Lawan, Alexandra Leeper, Nicky Pouw, Melanie Siggs, Kazumi Wakita

Bibliographic record

VenueMarine Policy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
FundersOrganisation de Coopération et de Développement ÉconomiquesHeriot-Watt University
KeywordsProsperityLivelihoodSustainabilityIntergenerational equityEquity (law)DignityFisheries lawSustainable development

Abstract

fetched live from OpenAlex

Contemporary fisheries have been shaped by a paradigm of perpetual growth, characterized by increasing global production and consumption. While this growth has driven economic benefits and technological progress, it has jeopardized the sustainability of marine ecosystems, with implications for the long-term livelihoods and wellbeing of fishers, consumers and resource dependent coastal populations worldwide. This paper advocates for a shift beyond growth towards a wellbeing economy. It considers how five fundamental principles intrinsic to a wellbeing economy - purpose, nature, fairness, participation and dignity - can help reorient the fisheries sector. The paper then provides ten actionable recommendations for reshaping the composition and structure of economic activity in fisheries to enhance societal wellbeing and equity within ecological boundaries. In a world grappling with the consequences of unchecked economic growth, this paper offers insights into fostering a regenerative fisheries system that safeguards human prosperity and environmental integrity.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0080.014
Open science0.0010.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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