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Record W3201089313 · doi:10.3138/jcs-2020-0006

Tracing One Warm Line: Climate Stories and Silences in Northwest Passage Tourism

2021· article· en· W3201089313 on OpenAlexvenueaboutno aff
Jenny Kerber

Bibliographic record

VenueJournal of Canadian Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismCruiseArcticClimate changeClimate justiceWitnessPolitical scienceEconomyGeographyOceanographyLawGeologyEconomics

Abstract

fetched live from OpenAlex

This article examines representations of polar cruise tourism in the Northwest Passage as climate change extends the geographic range of open waters and increases the number of ice-free days in the Canadian Arctic. It connects current cruise promotion to earlier exploration histories and investigates the paradoxes that arise in the drive to bear witness to climate change while accelerating its impacts through carbon-intensive travel. It also examines some of the ways that Franklin expedition tourism in particular is being used to reinforce claims of Canadian sovereignty over Arctic resources. Overall, the promotion of this kind of maritime tourism highlights many of the key fault lines between visitor expectations and geophysical and cultural realities in a changing North, raising doubts about whether expanded development of such tourism can succeed in creating climate change ambassadors. The article concludes that the potential for developing cross-cultural environmental justice solidarities depends in significant measure on the tourism industry’s greater inclusion of Inuit perspectives that understand the Arctic not merely as a place to travel through, but as a homeland of earth, sea, and the shifting ice between.

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)0.005
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0340.039
Scholarly communication0.0090.008
Open science0.0020.008
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.335
Teacher spread0.280 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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