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Record W4416552518 · doi:10.5070/t8.43245

Racial Performance at Sea: Race, Region, and Empire on the Empress of Australia

2025· article· W4416552518 on OpenAlexaboutno aff
Felicia Bevel

Bibliographic record

VenueJournal of Transnational American Studies · 2025
Typearticle
Language
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)DepictionContext (archaeology)EmpireMeaning (existential)Order (exchange)British Empire

Abstract

fetched live from OpenAlex

This article examines the 1928–1929 world cruise of the Empress of Australia, a ship owned by the Canadian Pacific Railway Company. Drawing upon concert programs, passenger accounts, Canadian Pacific official publications, and historical newspapers, it focuses on a concert that happened while the ship was at sea. It examines three songs—“Ol’ Man River,” “Hawaiian Memories,” and “Chu Chin Chow”—to show how nostalgia for the American South found purchase beyond US borders next to representations of a mysterious “Orient” and tropical Pacific. Situating this voyage within the larger context of Canadian nationhood and the Canadian Pacific’s investment in the British empire, I argue that the combination of these songs on a ship bound for sites of white empire did two things. One, it reassured white passengers that the racial order of the southern past would continue in the present. Second, it reinforced their experiences in China, Hawai'i, and other stops during the cruise. Through their depiction of Blacks and Asians as submissive and alien, these songs collectively romanticized and exoticized people of color and the places they inhabited—whether living in the wake of slavery in the American South or under US and British imperialism in the Pacific. Ultimately, this article demonstrates the importance of the transnational for understanding the export of nostalgic representations of the American South and how they adopted new meaning outside of a US context.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.008
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 head0.042
GPT teacher head0.346
Teacher spread0.304 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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