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Record W2078098802 · doi:10.1108/14777831211232209

Participatory issues in fisheries governance in Europe

2012· article· en· W2078098802 on OpenAlexaff
Cristina Pita, Ratana Chuenpagdee, Graham J. Pierce

Bibliographic record

VenueManagement of Environmental Quality An International Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCorporate governanceEuropean unionSustainabilityEmpowermentVariety (cybernetics)Citizen journalismProcess (computing)Fisheries managementBusinessEnvironmental resource managementEnvironmental planningFisheryPolitical scienceEconomicsGeographyComputer scienceEcologyEconomic growth

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to describe the fisheries governance system in the European Union (EU) and review fishers’ participation in the decision‐making process in the EU. Design/methodology/approach The study was based on a variety of sources, such as review of the literature, including scientific articles and reports, and data collected by the Coastal Transects Analysis Model (CTAM) online decision support tool. Findings The review reveals major improvements in involving fishers in the decision‐making process in Europe, but participation and empowerment are still generally lacking. Social implications The lack of fisher participation in the decision‐making process leads to limited acceptance of management measures which in turn results in management objectives not being met, with negative effects on environmental, economic and social sustainability. Originality/value The paper provides a review of participation in the EU decision‐making process. The results could give management bodies an insight into the failures of participation and point to possible ways forward.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.048
GPT teacher head0.307
Teacher spread0.259 · 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 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

Citations29
Published2012
Admission routes1
Has abstractyes

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