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Record W2803959016 · doi:10.3138/jcs.2016-0008.r3

Explorer on the Trans-Canada: Cold War Tourism and the Early Years of the Canadian Highway

2018· article· en· W2803959016 on OpenAlexvenueaboutno aff
Anna Sajecki

Bibliographic record

VenueJournal of Canadian Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismAdventureTourismColonialismVisionCold warConvictHistoryAmericanizationMedia studiesSociologyLawArt historyAnthropologyPolitical scienceArchaeologyPolitics

Abstract

fetched live from OpenAlex

This article asks what happens when an image of the Canadian nation marketed to American tourists also simultaneously helps produce a vision of highway nationalism in Canada. Starting with a primary discursive analysis of Trans-Canada Highway tourist brochures, I then address three Canadian travelogues from the period—Edward McCourt’s The Road across Canada (1965), Walter Stewart’s series for Star Weekly magazine (1965), and John M. Mitchell’s Coast to Coast across Canada (1967). While the travelogues implicitly replicate the same visions of the colonial adventurer family attached to the brochures—which I contextualize according to Western Cold War middle-class presentations of freedom and consumption—simultaneously McCourt’s and Stewart’s texts foreground discomfort with the tourist industry’s Americanization of nation-space along the highway. By celebrating Western nuclear family values through the image of colonial exploration, while also questioning the integrity of an Americanizing tourist industry, McCourt’s and Stewart’s travelogues—published just a few years after the Trans-Canada Highway officially opened in 1962—realize the way early representations of the highway were marked by a co-opted nationalism where the means of nationalist differentiation simultaneously encouraged a feedback loop of American imperialism.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.221
Teacher spread0.193 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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