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Record W2138864332

Perspectives of Decision Makers and Regulators on Climate Change and Adaptation in Expedition Cruise Ship Tourism in Nunavut

2012· article· en· W2138864332 on OpenAlexafffundabout
Adrianne Johnston, Margaret Johnston, Emma Stewart, Jackie Dawson, Harvey Lemelin

Bibliographic record

VenueLincoln University Research Archive (Lincoln University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of OttawaLakehead University
FundersSocial Sciences and Humanities Research Council of CanadaArcticNetUniversity of Guelph
KeywordsTourismContext (archaeology)Government (linguistics)Climate changeLegislationStakeholderCruiseCorporate governanceBusinessEnvironmental resource managementArcticPublic relationsEnvironmental planningPolitical scienceMarketingGeographyEconomicsEngineeringFinanceEcology
DOInot available

Abstract

fetched live from OpenAlex

Increases in Arctic tourism over the past few decades have occurred within a context of change, including climate change. This article examines the ways in which tourism decision makers and regulators in Nunavut view the interactions of cruise ship tourism and climate change, the challenges presented by those interactions, and the opportunities available within this context of change. The article uses an approach that is aimed at assessing sensitivities and adaptive capacity in order to develop strategies for managing change. It describes the findings from thirty-one semi-structured interviews conducted with federal government, Government of Nunavut, and industry personnel and managers involved in Nunavut’s tourism industry. The two major themes of the article are the growth and adjustment in the cruise tourism industry stemming from climate change and the governance issues that are associated with these changes. A strong focus in the interviews was recognition of the need for a collaborative approach to managing the industry and the need to enhance and extend territorial legislation to ensure a safe and coordinated industry to provide benefit to Nunavut, the communities that host the ships, the industry, and the tourists. The article concludes that decision makers and regulators need to address the compounding of challenges arising from tourism and climate change through a multi-level stakeholder approach.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
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 head0.094
GPT teacher head0.338
Teacher spread0.244 · 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 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 routes3
Has abstractyes

Explore more

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