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Record W2141983421 · doi:10.1017/s106828050000575x

Socioeconomics of Individual Transferable Quotas and Community-Based Fishery Management

2004· article· en· W2141983421 on OpenAlexaff
Parzival Copes, Anthony Charles

Bibliographic record

VenueAgricultural and Resource Economics Review · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsSaint Mary's UniversitySimon Fraser University
Fundersnot available
KeywordsFisheries managementEnforcementBusinessSustainabilitySocioeconomic statusGovernment (linguistics)FisheryEnvironmental resource managementFlexibility (engineering)EconomicsNatural resource economicsPublic economicsEcologyFishingPopulation

Abstract

fetched live from OpenAlex

In many fisheries around the world, the failures of centralized, top-down management have produced a shift toward co-management —collaboration and sharing of decision making between government and stakeholders. This trend has led to a major debate between two very different co-management approaches— community-based fishery management and market-based individual transferable quota management. This paper examines the debate over the relative merits of these models and undertakes a socioeconomic analysis of the two approaches. The paper includes (1) an analysis of differences in the structure, philosophical nature, and underlying value systems of each, including a discussion of their treatment of property rights; (2) a socioeconomic evaluation of the impacts of each system on boat owners, fishers, crew members, other fishery participants, and coastal communities, as well as the distribution of benefits and costs among fishery participants; and (3) examination of indirect economic effects that can occur through impacts on conservation and fishery sustainability. The latter relate to ( a ) the conservation ethic, ( b ) the flexibility of management, ( c ) the avoidance of waste, and ( d ) the efficiency of enforcement. The paper emphasizes the need for a broader approach to analyzing fishery management options, one that recognizes and properly assesses the diversity of choices, and that takes into account the interaction of the fishery with broader community and regional realities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.440

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.212
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations116
Published2004
Admission routes1
Has abstractyes

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