An Introduction to the Sociology of Sports Mega-Events
Bibliographic record
Abstract
Acknowledgements. 1. An introduction to the sociology of sports mega-events: John Horne (University of Edinburgh, UK) and Wolfram Manzenreiter (University of Vienna, Austria). Part 1: Sports mega-events, modernity and capitalist economies. 2. Mega-events and modernity revisited: Maurice Roche (University of Sheffield, UK). 3. The Economic Impact of Major Sport Events: Chris Gratton, Simon Shibli, and Richard Coleman (Sport Industry Research Centre, Sheffield Hallam University, UK). 4. Urban entrepreneurship, corporate interests and sports mega-events: C. Michael Hall (University of Otago, New Zealand). Part 2: The Glocal Politics of Sports Mega-events. 5. Underestimated costs and overestimated benefits? Comparing the outcomes of sports mega-events in Canada and Japan: David Whitson (University of Alberta, at Edmonton, Canada) and John Horne (University of Edinburgh). 6. Modernizing China in the Olympic spotlight: China's national identity and the 2008 Beijing Olympiad: Xin Xu (Ritsumeikan Asia Pacific University, Japan). 7. The 2010 Football World Cup as a political construct: the challenge of making good on an African promise: Scarlett Cornelissen (University of Stellenbosch, South Africa) and Kamilla Swart (Cape Peninsula University of Technology, Cape Town, South Africa). Part 3: Power, spectacle and the city. 8. UEFA Euro 2004 Portugal: The social construction of a sports mega- event and spectacle: Salome Marivoet (University of Coimbra, Portugal). 9. Sports spectacles, uniformities and the search for identity in late modern Japan: Wolfram Manzenreiter (Vienna University). 10. Deep play: Sports mega-events and urban social conditions in the U.S.A: Kimberly Schimmel (Kent State University, U.S.A.). 11. Olympic Urbanism and Olympic Villages: Planning strategies in olympic host cities, London 1908 - London 2012: Francesc Munoz (Universitat Autonoma de Barcelona, Spain). Notes on contributors. Index.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".