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Record W2899176590 · doi:10.1111/faf.12332

Managing at Maximum Sustainable Yield does not ensure economic well‐being for artisanal fishers

2018· article· en· W2899176590 on OpenAlexaff
Alfredo Girón‐Nava, Andrew F. Johnson, Andrés M. Cisneros‐Montemayor, Octavio Aburto‐Oropeza

Bibliographic record

VenueFish and Fisheries · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersDivision of Environmental BiologyConsejo Nacional de Ciencia y Tecnología
KeywordsMaximum sustainable yieldEconomic rentPer capitaFishingSustainabilityRevenueOverfishingYield (engineering)FisheryNatural resource economicsPovertyBusinessWork (physics)Natural resourceSustainable yieldFisheries managementEconomicsEconomic growthEcologyPopulationFinance

Abstract

fetched live from OpenAlex

Abstract Maximum Sustainable Yield ( MSY ) is a common target for fisheries aiming to achieve long‐term ecological sustainability. Although achieving MSY may ensure the long‐term sustainability of fish populations, we ask whether it will provide economic security for fishers. Here we use 16 years of daily landing records to estimate potential catches and revenues per capita if fisheries were exploited at MSY in 11 subregions across Mexico. We then compare fishers’ estimated revenues per capita against national poverty limits at the household level. Our results show that even if MSY is reached in artisanal fisheries, the overcapacity of fleets and the dissipation of rents threatens the economic well‐being of fishers and their families, pushing revenues per capita below poverty levels. Our work demonstrates the importance of resolving the trade‐offs between achieving economic, social and environmental objectives when managing for the long‐term sustainable use of natural resources.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.593
Threshold uncertainty score0.978

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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.

Study designNot applicable
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

Citations31
Published2018
Admission routes1
Has abstractyes

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