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Record W3114080615 · doi:10.4337/9781788119597.00022

Polar cruise tourism

2020· book-chapter· en· W3114080615 on OpenAlexaboutno aff
Daniela Liggett, Emma Stewart

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismCruiseAntarctic treatyArcticThe arcticConventionGeographyPolitical sciencePolar codeRegional scienceOceanographyBusinessPolarLawGeology

Abstract

fetched live from OpenAlex

This Chapter offers a critical discussion of the legal framework for regulating tourism to the Arctic and Antarctic, in particular ship-borne tourism. Two case studies offer an in-depth analysis of the current regulatory mechanisms and explore how well suited they are to address the rapidly changing nature of Polar tourism activities. Our first case study focuses on Arctic Canada, where significant regulatory complexity presents significant barriers to entry for new tourism operators. Our second case study concerns the Ross Sea region, where a short season and the destination’s remoteness limit the number of operators. Tourism here, and across the entire Antarctic region, is subject to high-level regulation under the Antarctic Treaty System as implemented within national jurisdictions. The Chapter examines the interplay of international regulation through, for instance, the International Maritime Organization’s Polar Code or the United Nations Convention on the Law of the Sea, and national policies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.155

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 studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0460.010

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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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