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Record W1991155822 · doi:10.1080/1088937x.2012.666768

Policies and practicalities of shipping in arctic waters: Inuit perspectives from Cape Dorset, Nunavut

2012· article· en· W1991155822 on OpenAlexaffabout
Karen E. Kelley, Gita Ljubicic

Bibliographic record

VenuePolar Geography · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton UniversityInuit Tapiriit Kanatami
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsArcticThe arcticCapePoliticsEnvironmental planningPolitical scienceEnvironmental resource managementGeographyBusinessOceanographyEnvironmental protectionEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Under changing climatic conditions, political and economic interest in accessing arctic waters and shipping potential is increasing, bringing forward opportunities and challenges that need to be addressed. To explore the practical implications of this from Inuit perspectives, we undertook a community-based case study in Cape Dorset, Nunavut, conducting 20 interviews between 2008 and 2009. We also examined three Nunavut Acts and Agreements, three Canadian Federal Acts, and one International Agreement to better understand the regulations and policy positions. For each we sought to understand characterizations of: community uses of the marine environment; impacts of shipping; and monitoring of ship travel. Our objective was to investigate how well (or not) Inuit experiences and observations of arctic shipping were incorporated into policy provisions governing shipping in arctic waters. A comparative analysis of Inuit and policy positions shows shared and unique priorities as well as areas of apparent disconnect related to shipping in arctic waters. Therefore, this article highlights key considerations for arctic shipping from both practical and policy perspectives. We conclude with recommendations relating to undertaking community consultations, establishing the Marine Council, improving emergency response measures, and updating existing Acts and Agreements in an effort to contribute to ameliorate the situation for mutual benefit.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.007
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.347
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations21
Published2012
Admission routes2
Has abstractyes

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