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Record W4283524056 · doi:10.1016/j.marpol.2022.105179

A framework for investigating commercial license and quota holdings in an era of fisheries consolidation, concentration and financialization

2022· article· en· W4283524056 on OpenAlexafffundabout
Jennifer J. Silver, Joshua S. Stoll

Bibliographic record

VenueMarine Policy · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLicenseBusinessConsolidation (business)JurisdictionFisheryEconomicsFinance

Abstract

fetched live from OpenAlex

This paper proposes a novel framework to conceptualize and investigate license and quota holdings within fisheries jurisdictions and regions. We motivate and develop the framework by drawing together two bodies of literature that have been relatively disparate: one on fisheries industrialization and the other, from food studies and food systems scholars, on consolidation and concentration in farming and agrifood value chains. An important observation from food studies and food systems research is that a spectrum of logics -- from productive through to speculative -- motivate farmland accumulation and shape patterns of ownership, investment, and power in farming regions. We apply the framework to investigate license holding portfolios within the federally-managed fisheries jurisdiction off of Canada’s western-most province, British Columbia (BC). We calculate the market value of a selection of large portfolios. We also begin to explore power dynamics, finding that those with large and valuable portfolios are uniquely positioned to exercise and expand control within the jurisdiction. Fisheries and marine policy researchers should assume that hedge funds, private investment and equity firms, and other publicly traded businesses now regularly assess the investment potential of licenses and quota; case-based, comparative, and policy-oriented research are all urgently needed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.274

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 head0.027
GPT teacher head0.258
Teacher spread0.231 · 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 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

Citations9
Published2022
Admission routes3
Has abstractyes

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