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Record W2983291559 · doi:10.1080/02722011.2019.1660454

Cultural Nationalism, Anti-Americanism, and the Federal Defense of the Canadian Football League

2019· article· en· W2983291559 on OpenAlexaffabout
John Valentine

Bibliographic record

VenueThe American Review of Canadian Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsMacEwan University
Fundersnot available
KeywordsFootballNationalismLeagueSovereigntyGovernment (linguistics)Political scienceNational identityAmericanizationPublic administrationLawPolitical economySociologyPolitics

Abstract

fetched live from OpenAlex

During the 1960s nationalism flourished in Canada as did American influence, both cultural and economically, as well as separatist sentiment in Quebec. The Canadian federal government became more interventionist to combat threats to Canadian sovereignty: internal threats from Quebec and external threats from the United States. The federal government used sport as a nation-building tool and eventually acted to protect the Canadian Football League (CFL) as a display of resistance to Americanization and in an attempt to unite French and English. Canadian football had become a symbol of the nation and therefore could be used by the government in a symbolic way to resist cultural imperialism and promote national unity. On two occasions the federal government acted to ensure the CFL preserved its Canadian identity; first, to prevent Canadian-based football teams from joining an American professional football league, and second, to prevent American-based teams from joining the CFL. John Munro was the key Canadian politician who formulated policy to protect Canadian football.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.048
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0070.014
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.325
Teacher spread0.285 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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