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Record W2966574913 · doi:10.1080/1088937x.2019.1648585

Cruising the marginal ice zone: climate change and Arctic tourism

2019· article· en· W2966574913 on OpenAlexaff
David Palma, Alix Varnajot, Kari Dalen, Ilker K. Basaran, Charles Brunette, Marta Bystrowska, Anastasia Korablina, Robynne Nowicki, Thomas A. Ronge

Bibliographic record

VenuePolar Geography · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsClimate changeTourismArcticArctic ice packOceanographyThe arcticSea iceClimatologyPhysical geographyGeographyEnvironmental scienceGeologyArchaeology

Abstract

fetched live from OpenAlex

The effects of climate change are leading to pronounced physical and ecological changes in the Arctic Marginal Ice Zone (MIZ). These are not only of concern for the research community but also for the tourism industry dependent on this unique marine ecosystem. Tourists increasingly become aware that the Arctic as we know it may disappear due to several environmental threats, and want to visit the region before it becomes irrevocably changed. However, ‘last-chance tourism’ in this region faces several challenges. The lack of infrastructure and appropriate search and rescue policies are examples of existing issues in such a remote location. Additionally, tourism itself may further amplify the physical and ecological changes in the Arctic region. In this article, we provide an interdisciplinary analysis of the links between the MIZ, climate change and the tourism industry. We also identify existing regulations and the need for new ones concerning operations in the MIZ and in the Arctic Ocean.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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

Citations101
Published2019
Admission routes1
Has abstractyes

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