Surveilling and securing the Olympics : from Tokyo 1964 to London 2012 and beyond
Bibliographic record
Abstract
PART I: PROLOGUE Prologue. Olympic Surveillance as a Prelude to Securitization Don Handelman PART II: INTRODUCTION 1. Olympic Games as Complex Planned Event: Between Uncertainty and Through Security Meta-Ritual Vida Bajc 2. On Security and Surveillance in the Olympics: A View from Inside the Tent Richard Pound PART III: CASE STUDIES 3. Modernity and the Carnivalesque (Tokyo 1964) Christian Tagsold 4. Repression of Protest and the Image of Progress (Mexico City 1968) Kevin B. Witherspoon 5. Fear of Radical Movements and Policing the Enemy Within (Sapporo 1972) Kiyoshi Abe 6. The Most Beautiful Olympic Games that Were Ever Destroyed (Munich 1972) Jorn Hansen 7. The Armys Presence Will Be Obvious (Montreal 1976) Bruce Kidd 8. To Guarantee Security and Protect Social Order (Moscow 1980) Carol Marmor-Drews 9. Cross-National Intelligence Cooperation and Centralized Security Control System (Seoul 1988) Gwang Ok and Kyoung Ho Park 10. Platform for Local Political Expression and Resolution (Barcelona 1992) Stephen Essex 11. Audience-Spectator-Performer Interactions (Lillehammer 1994) Ingrid Rudie 12. National Special Security Event (Salt Lake City 2002) Sean P. Varano, George Burruss, Jr. and Scott H. Decker 13. Asymmetric Power Relations (Athens 2004) Anastassia Tsoukala 14. Spatialities of Security and Control (Turin 2006) Alberto Vanolo 15. Peoples Olympics? (Beijing 2008) Gladys Pak Lei Chong, Jeroen de Kloet and Zeng Guohua 16. Promoting Civility, Excluding the Poor (Vancouver 2010) Jacqueline Kennelly 17. Public-Private Global Security Assemblages (London 2012) Joseph R. Bongiovi
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".