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Record W2295344536 · doi:10.1177/0163443716635859

Power plays and Olympic divisions: bilingualism and the politics of Canadian viewing rights at the 2010 Winter Olympic Games

2016· article· en· W2295344536 on OpenAlexaffabout
Jay Scherer, Jean Harvey, Marcela Hofman-Mourão

Bibliographic record

VenueMedia Culture & Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsPoliticsLegislationPolitical scienceState (computer science)LawPublic administrationMedia studiesSociologyPolitical economy

Abstract

fetched live from OpenAlex

There have been innumerable political debates around the world over the distribution of live sporting events in the digital era. Typically, these deliberations involve competing claims and interests associated with sport, commerce, cultural and broadcasting policies, and, at times, language rights. This article examines a recent debate in Canada over unequal access to live French-language coverage of the 2010 Vancouver Winter Olympic Games that prompted several interventions by public authorities and francophone community organizations. These interventions were required precisely because of the economic structure of the broadcasting market and the historical conditions of Canadian broadcasting policy. Indeed, unlike other nations, the Canadian state has not enacted legislation to protect the ‘viewing rights’ of citizens to have access to live telecasts of sporting events of national significance in both official languages. The debate was significant, then, precisely because it revolved around a central political commitment of the state: the principle of bilingualism. Moreover, the deliberations underscored the historical tensions between the material interests of the sport–media complex and the viewing rights of Canadians to have access to telecasts of sport in both official languages, and, by extension, the constitutional language rights of citizens enshrined in the Official Languages Act (1969).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.970

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 studies0.0010.002
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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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