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Record W2170920380 · doi:10.1017/s0032247412000484

Of whales and oil: Inuit resource governance and the Arctic Council

2013· article· en· W2170920380 on OpenAlexaboutno aff
Jessica M. Shadian

Bibliographic record

VenuePolar Record · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticCorporate governanceIndigenousWhalingPolitical scienceSafeguardingResource (disambiguation)The arcticSovereigntyNormativeCommissionEnvironmental planningResource management (computing)Environmental resource managementBusinessEnvironmental ethicsGeographyOceanographyLawPoliticsEconomicsEcologyArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT This article takes a normative approach to explore what and how we might learn from existing indigenous governance arrangements in the Arctic and how they may contribute to the larger debates over Arctic governance and who decides. It begins with a brief exploration of the existing literature regarding co-management; particularly what some legal scholars have defined as post-Westphalian resource management as well as engaging ongoing discussions about co-management as it pertains to the Arctic. It then turns to the Alaska Eskimo Whaling Commission (AEWC) as a case study and possible starting point for governing newly emerging resource management issues in the Arctic. Specifically, this article will look at how the governance framework of the AEWC might be applicable for the current governance discussions regarding Arctic offshore oil and gas development. Lastly, this paper will offer preliminary reflections as to how a post-sovereign resource management approach could contribute to the broader theoretical debates concerning who owns the Arctic and who decides. Specifically it offers one possible way to envisage the future of a strengthened Arctic Council operating in a world where states are not the only actors participating in the governance of the Arctic.

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)0.001
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 score0.832
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.254
Teacher spread0.227 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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