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Record W4400664243 · doi:10.1080/09669582.2024.2370971

A virtual geobibliography of polar tourism and climate change

2024· article· en· W4400664243 on OpenAlexfundno aff
O. Cenk Demiroglu, Dorothee Bohn, Halvor Dannevig, C. Michael Hall, Christy Hehir, Linda Lundmark, Robert O. Nilsson, Julia Olsen, Kaarina Tervo-Kankare, Marisol Vereda, Johannes Welling

Bibliographic record

VenueJournal of Sustainable Tourism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersCenter for Clinical and Translational ResearchUniversity of the ArcticSvenska Forskningsrådet FormasVancouver Island University
KeywordsClimate changeTourismContext (archaeology)ArcticThematic mapGeographyEnvironmental resource managementPolitical scienceEnvironmental scienceCartographyOceanographyGeology

Abstract

fetched live from OpenAlex

The polar regions are increasingly at the center of attention as the hot spots of climate crisis as well as tourism development. The recent IPCC reports highlight several climate change risks for the rather carbon-intensive and weather-based/dependent polar tourism industry in the Arctic and the Antarctic. This study presents the scholarly state-of-knowledge on tourism and climate change in the polar regions with a literature survey extending beyond the Anglophone publications. As a supporting tool, we provide a live web GIS application based on the geographical coverages of the publications and filterable by various spatial, thematic and bibliographical attributes. The final list of 137 publications indicates that, regionally, the Arctic has been covered more than the Antarctic, whilst an uneven distribution within the Arctic also exists. In terms of the climate change risks themes, climate risk research, i.e. impact and adaptation studies, strongly outnumbers the carbon risk studies especially in the Arctic context, and, despite a balance between the two main risk themes, climate risk research in the Antarctic proves itself outdated. Accordingly, the review ends with a research agenda based on these spatial and thematic gaps and their detailed breakdowns.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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