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Record W2162910499 · doi:10.1080/19425120.2012.690825

Challenges for Implementing an Ecosystem Approach to Fisheries Management

2012· article· en· W2162910499 on OpenAlexaff
James H. Cowan, Jake Rice, Carl J. Walters, Ray Hilborn, Timothy E. Essington, John W. Day, Kevin M. Boswell

Bibliographic record

VenueMarine and Coastal Fisheries · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaGovernment of CanadaFisheries and Oceans Canada
Fundersnot available
KeywordsFisheries managementEcosystem approachFisheries lawEcosystemEcosystem managementPaceEcosystem-based managementFisheries scienceBusinessFisheryEnvironmental resource managementEcosystem servicesFishingEconomicsEcologyGeography

Abstract

fetched live from OpenAlex

Abstract The ecosystem approach is being promoted as the foundation of solutions to the unsustainability of fisheries. However, because the ecosystem approach is broadly inclusive, the science for its implementation is often considered to be overly complex and difficult. When the science needed for an ecosystem approach to fisheries is perceived this way, science products cannot keep pace with fisheries critics, thus encouraging partisan political interference in fisheries management and proliferation of “faith-based solutions. In this paper we argue that one way to effectively counter politicization of fisheries decision-making is to ensure that new ecosystem-based approaches in fisheries are viewed only as an emergent property of innovation in science and policy. We organize our essay using three major themes to focus the discussion: empirical, jurisdictional, and societal challenges. We undertake at least partial answers to the following questions: (1) has conventional fisheries management really failed?; (2) can short-comings in conventional fisheries management be augmented with new tools, such as allocation of rights?; (3) is the Ecosystem Approach to Fisheries (EAF) equivalent to Ecosystem-Based Management?; and (4) is restoration of degraded ecosystems a necessary component of an EAF? Received March 6, 2012; accepted April 26, 2012

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 score0.883
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.257
Teacher spread0.210 · 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

Citations71
Published2012
Admission routes1
Has abstractyes

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