Bibliographic record
Abstract
Since Munich, one of the key features of Olympic security has been the use of technological surveillance, a strategy that has become increasingly central to securing large sporting events in the post-9/11 era and one that fits neatly with the IOC’s demands to prioritise the sporting event over the policing spectacle. With the London Games likely to become the first biometric and wireless Olympics, the capital is likely to reinforce its reputation as a pioneer of such technologies via the deployment of ever-more intensified, networked and advanced forms of technological observation. This chapter examines the way technological surveillance has been applied to secure mega sporting events (with particular reference to the post-Munich Olympiads) and considers the implications of these processes and practices for 2012. In doing so, key processes shaping the form and scale of surveillance strategies and the types of technological surveillance provision (from first generation CCTV systems to second generation video analytics) are identified and analysed before their application, impact, efficacy and legacy are critically assessed. As this chapter argues, a broad view of Olympic security operations reveals the convergence of at least three different processes. First, a number of security themes have been transferred across events and locations. Second, their direction and intensity have also been shaped by responses to key events, most notably, Munich, the 1996 Atlanta bombing and 9/11. At the same time, threats to Olympics have constantly shifted in relation to their own contextual environments and logic, thus raising operational questions over the relationship between future planning and retrospective events. In exploring the role and impact of surveillance strategies in securing the Olympics from crime and terrorism, this chapter engages in three main areas of discussion. First, a number of key contextual issues are addressed. This involves a definition of what is meant by ‘surveillance’ and an exploration of surveillance strategies in the areas hosting the 2012 Games (both nationally and locally). Second, the chapter will focus on the ways sporting events, particularly the Olympic Games, have been secured using surveillance technologies. Finally, the chapter will draw on some of the key analytical issues emerging from these discussions to consider the efficacy, operational context and ethical considerations of surveillance strategies at Olympic sized events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".