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Record W2045238610 · doi:10.1068/a45202

Alternative Regimes of Transnational Environmental Certification: Governance, Marketization, and Place in Alaska's Salmon Fisheries

2013· article· en· W2045238610 on OpenAlexaff
Paul Foley, Karen Hébert

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsMemorial University of Newfoundland
FundersU.S. Fish and Wildlife Service
KeywordsCertificationCorporate governanceStewardship (theology)LegitimacyMarketizationEnvironmental governancePoliticsGrassrootsPolitical sciencePublic administrationPolitical economyBusinessSociologyLaw

Abstract

fetched live from OpenAlex

Transnational certification and ecolabeling programs have become an important new site of environmental governance, as well as an emerging arena for action and conflict in international trade. This paper explores the implementation of Marine Stewardship Council (MSC) certification in salmon fisheries in the US state of Alaska in the early 2000s, the growing opposition within the industry to MSC certification through periods of reassessment, and the emergence of an alternative Alaska certification initiative in 2011. It suggests that these shifts were rooted in struggles over understudied third-party certification and labeling processes that we conceptualize as marketized governance. The paper further shows how certification and labeling can obscure and expose competing industry interests and power relations, provoke struggles over fisheries' social representation, and open up novel avenues for cooperation and change, indicating ambiguity in the social and cultural implications of neoliberal modes of governance. Finally, the paper suggests that the ascendancy of the MSC has sparked the emergence of new political and economic geographies of certification and ecolabeling in Alaska and other jurisdictions where place-specific and other initiatives vie for governance and market legitimacy.

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 score0.015
Threshold uncertainty score0.503

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 science0.0000.000
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 head0.009
GPT teacher head0.188
Teacher spread0.178 · 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

Citations52
Published2013
Admission routes1
Has abstractyes

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