The Symbolic Politics of Sport Mega-Events: 2010 in Comparative Perspective
Bibliographic record
Abstract
For ambitious civic and national boosts sport mega-events provide unique opportunities for the pursuit of symbolic politics—a chance to signal important changes of direction, reframe dominant narratives about the host, and/or reinforce key messages of change. These signals or narratives are critical vehicles of legitimation, with both narrowly instrumental objectives and more expansive purposes related to the mobilisation of societal support for certain dominant ‘ideas of the state’. This paper explores the realm of symbolic politics through a comparative analysis of three disparate mega-event hosts which will take the world stage in 2010: South Africa (the FIFA World Cup), Delhi/India (the Commonwealth Games), and Vancouver/Canada (the Winter Olympics). The paper argues that despite important differences in the circumstances of these hosts and the events they are to mount, there are some key commonalities in the narratives they seek to deploy and the subtexts they embody. These commonalities revolve around a paradoxical blending of inclusive, transcendent, or cosmopolitan narratives on the one hand, and competitive, differentiating narratives of ‘world class’ aspirations and achievements, on the other. Strikingly then, these widely dispersed events have become vehicles for similar messages with potentially contradictory implications.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.000 |
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.
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".