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Record W2030585035 · doi:10.1080/17430437.2013.806039

Globalization, corporate nationalism and masculinity in Canada: sport, Molson beer advertising and consumer citizenship

2013· article· en· W2030585035 on OpenAlexaboutno aff
Steven J. Jackson

Bibliographic record

VenueSport in Society · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityNationalismGlobalizationContext (archaeology)Multinational corporationCitizenshipSociologyConsumption (sociology)Gender studiesPolitical scienceAdvertisingSocial scienceLawBusinessPolitics

Abstract

fetched live from OpenAlex

Within the context of globalization, nations have increasingly become the object of both production and consumption. Consequently, directly or indirectly citizens are being conceptualized, appealed to and transformed into consumers. A key driving force in this transformation is the diverse range of multinational corporations (MNCs) that engage in what is referred to as corporate nationalism – a process that seeks to capitalize upon the nation as a source of collective identification. This paper sets forth to explore (1) the nature and significance of corporate nationalism within the context of globalization; (2) the nature and significance of the ‘holy trinity’ – sport, beer and masculinity; (3) a case study of one specific Molson Canadian beer advertising campaign to illustrate how it serves as a manual of both masculinity and national identity in Canada; (4) the role of cultural intermediaries in reproducing dominant forms of masculinity; and (5) the implications of corporate nationalism and the holy trinity for understanding the reproduction of masculinity in an increasingly global world.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.096
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0230.010
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.017
GPT teacher head0.204
Teacher spread0.186 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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