Bibliographic record
Abstract
PART I: CONCEPTUAL ISSUES 1. From Legacy to Leverage Laurence Chalip 2. Megas for Strivers: the Politics of Second Order Events David Black 3. 'Legacy' Revisited Holger Preuss 4. Sports Mega-Events and Mass Participation in Sport Mike Weed 5. Neo-Liberalism and Sports Mega-Events Michael Silk PART II: PREVIOUS SPORTS MEGA-EVENT STRATEGIES 6. Sydney 2000 and Melbourne 2006: A Tale of Two Australian Cities Bob Stewart 7. 'Leaving Las Magas', or Can Sustainability ever be Social? Vancouver 2010 in Post-Political Perspective Caitlin Pentifallo and Robert Van Wynsberghe 8. Seoul 1998 Alan Bairner and Ji-Hyun Cho 9. The Legacy of the 2002 Shanghai Tennis Masters Cup Dongfeng Liu 10. London 2012 John Horne and Barrie Houlihan PART III: 'EMERGING STATES' AND SPORTS MEGA-EVENTS 11. Magical Thought and the Legacy Discourse of the 2008 Beijing Games Wolfram Manzenreiter 12. Dreaming Big: Spectacular Events and the 'World-Class' City - The Commonwealth Games in Delhi Amita Baviskar 13. South Africa's 'Coming Out Party': Reflections on the Significance and Implications of the 2010 FIFA World Cup Scarlett Cornelissen 14. Qatar, Global Sport, and the 2022 FIFA World Cup Paul Brannagan and Richard Giulianotti 15. Russia: Showcasing a 'Re-Emerging' State? Oleg Golubchikov and Irina Slepukhina
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.022 | 0.050 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".