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Record W1943703490 · doi:10.3138/topia.27.5

“A Portrait of This Country”: Whiteness, Indigeneity, Multiculturalism and the Vancouver Opening Ceremonies

2012· article· en· W1943703490 on OpenAlexvenueaboutno aff
Nathan Kalman-Lamb

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismIdentity (music)IndigenousGender studiesPerformative utteranceContext (archaeology)SociologyDiversity (politics)Media studiesAestheticsHistoryAnthropologyArtArchaeology

Abstract

fetched live from OpenAlex

In this article, I examine how the performance of Canadian identity in the Vancouver 2010 Olympic Winter Games opening ceremonies reflects the persistence of whiteness at the core of Canada’s multicultural identity. To this end, I analyze the cultural components of the Vancouver ceremonies and the coverage of this spectacle by Canadian television broadcaster CTV. This event is worthy of study because it marks a crucial moment of nation-building and representation for the Canadian nation before the world. In this formative and performative moment, organizers did not foreground multiculturalism as central to Canadian identity— problematic as liberal multiculturalism may be in the context of a structurally inequitable society. Instead, they chose to portray whiteness as the core of Canadian identity. While there is a significant acknowledgment of Canada’s Indigenous peoples in the ceremonies, this should not be seen as a celebration of diversity. Rather, Indigineity is appropriated as the origin of a national teleology that culminates in whiteness. Indeed, by locating the nation’s origins in Indigenous societies, the claim can be made that Canada is essentially multicultural, and thus, that no further representation or acknowledgment of non-white people is necessary.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
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 studies0.0020.002
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.278
Teacher spread0.249 · 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 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

Citations6
Published2012
Admission routes2
Has abstractyes

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