MétaCan
Menu
Back to cohort
Record W2071069509 · doi:10.3727/154427307784772057

Climate Change, Marine Tourism, and Sustainability in the Canadian Arctic: Contributions from Systems and Complexity Approaches

2007· article· en· W2071069509 on OpenAlexfundaboutno aff
Jackie Dawson, Patrick Maher, Scott Slocombe

Bibliographic record

VenueTourism in Marine Environments · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTourismSustainabilityClimate changeArcticEnvironmental resource managementEnvironmental planningGeographyRegional scienceEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The climate-sensitive tourism industry, including Arctic marine tourism, is expected to be significantly impacted by climate change. The multifaceted impacts of climate change at multiple scales warrant a theoretical framework that is able to effectively examine complex and integrated relationships. The complex ties between culture, economy, and environment in the Arctic also mean a systems perspective on tourism-related change and sustainability seems highly appropriate. This article outlines some systems approaches to Arctic marine tourism. Key contributions of a systems framework include reconceptualizing Butler's Tourism Area Life Cycle, providing a framework for describing and understanding the tourism–climate change system, including identifying change influences and dynamics at varied spatial and temporal scales, and informing sustainability planning and assessment.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.125
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0040.012
Scholarly communication0.0070.002
Open science0.0010.002
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 head0.049
GPT teacher head0.291
Teacher spread0.242 · 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 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

Citations64
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueTourism in Marine EnvironmentsSame topicArctic and Russian Policy StudiesFrench-language works237,207