Sport and corporate nationalisms
Bibliographic record
Abstract
Introduction Sporting Capital: Multinational and Transnational Corporatism David L. Andrews, University of Maryland, Michael L. Silk, University of Maryland and C.L. Cole, University of Illinois Section One: Multinational Sporting Corporatism Professional Sport Teams, Global Logos, and the Global Media/Entertainment Industry: A Political Economy of Transnational Sport Jean Harvey and Alan Law, both at University of Ottawa Corporatizing Sport: Adidas, ISL and the Reshaping of Sports Political Economy Alan Tomlinson, University of Brighton Marketing Generosity: The Avon Worldwide Fund for Womens Health and the Reinvention of Global Corporate Citizenship Samantha King, Queens University SEGA Dreamcast: National Football Cultures and the New Europeanism Philip Rosson, Dalhousie University, Canada Fram Pac Bell to the Tokyo Dome: Baseball and Economic Nationalism Jeremy Howell, University of San Francisco Section Two: Transnational Sporting Corporatism Every Girls a Superhero: Corporate (Trans)Nationalism(s), Womens Soccer, and Global (W)USA Michael D. Giardina and Jennifer L. Metz, University of Illinois Imagining Benevolence and Nation: Tragedy, Sport and the Transnational Marketplace Mary G. McDonald, Miami University, Ohio Making it Local?: NBA Expansion and the English Basketball Subculture Mark Falcous, University of Otago and Joseph Maguire, Loughborough University Cultural Contradictions / Contradicting Cultures: The Corporate Transnationalization of China? Trevor Slack, University of Alberta, Michael L. Silk, University of Maryland and Fan Hong, DeMontfort University Sport, Tribes and Technology: The New Zealand All Blacks Haka and the Politics of Identity Steven J. Jackson and Brendan Hokowhitu, both at University of Otago, New Zealand Beyond Sport: Imaging and Re-imaging Guiness as a Global Brand John Amis, University of Memphis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".