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Record W2125453383 · doi:10.1123/ssj.27.1.1

Living With War: Sport, Citizenship, and the Cultural Politics of Post-9/11 Canadian Identity

2010· article· en· W2125453383 on OpenAlexaffabout
Jay Scherer, Jordan Koch

Bibliographic record

VenueSociology of Sport Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCitizenshipNational identityPoliticsPolitical scienceIce hockeyMedia studiesLeagueEmpireMythologyIdentity (music)NarrativeNationalismGender studiesSociologyPolitical economyLawAestheticsHistory

Abstract

fetched live from OpenAlex

If sport scholars are going to contribute to a critical (inter)national dialogue that challenges “official versions” of a post-9/11 geo-political reality, there is a need to continue to move beyond the borders of the US, and examine how nationalistic sporting spectacles work to promote local military initiatives that are aligned with the imperatives of neoliberal empire. In this article we provide a critical reading of the Canadian Broadcasting Corporation’s nationally-televised broadcast of a National Hockey League game, colloquially known as Tickets for Troops. We reveal how interest groups emphasized three interrelated narratives that worked to: 1) personalize the Canadian Forces and understandings of neoliberal citizenship, 2) articulate warfare/military training with men’s ice hockey in relation to various promotional mandates, and 3) optimistically promote the war in Afghanistan and the Conservative Party of Canada via storied national traditions and mythologies.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.095
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0420.039
Scholarly communication0.0130.003
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations30
Published2010
Admission routes2
Has abstractyes

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