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Record W2729876305 · doi:10.3390/resources6030025

Strategic Development Challenges in Marine Tourism in Nunavut

2017· article· en· W2729876305 on OpenAlexaffabout
Margaret Johnston, Jackie Dawson, Patrick Maher

Bibliographic record

VenueResources · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsCape Breton UniversityUniversity of OttawaLakehead University
Fundersnot available
KeywordsTourismCruiseCraftPleasureArcticBusinessCorporate governanceEnvironmental planningEnvironmental resource managementBoomTourism geographyPolitical scienceGeographyOceanographyEconomicsFinance

Abstract

fetched live from OpenAlex

Marine tourism in Arctic Canada has grown substantially since 2005. Though there are social, economic and cultural opportunities associated with industry growth, climate change and a range of environmental risks and other problems present significant management challenges. This paper describes the growth in cruise tourism and pleasure craft travel in Canada’s Nunavut Territory and then outlines issues and concerns related to existing management of both cruise and pleasure craft tourism. Strengths and areas for improvement are identified and recommendations for enhancing the cruise and pleasure craft governance regimes through strategic management are provided. Key strategic approaches discussed are: (1) streamlining the regulatory framework; (2) improving marine tourism data collection and analysis for decision-making; and (3) developing site guidelines and behaviour guidelines.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.006
Scholarly communication0.0070.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.340
Teacher spread0.230 · 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
Published2017
Admission routes2
Has abstractyes

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