Planning for the worst: risk, uncertainty and the Olympic Games
Bibliographic record
Abstract
Security for the Olympic Games has become undeniably visible in recent years. A certain degree of this visibility became unavoidable after the 1972 Munich Olympics when military personnel and hardware became standard elements of Olympic security. Yet, this visibility is qualitatively different today in that it is often deliberately fashioned for public consumption. This article argues that this expressive dimension of security at the Games provides a window into wider issues of how authorities 'show' that they can deliver on the promise of maximum security under conditions of radical uncertainty. The latter sections of this article examine three ways in which this promise is extended: the discursive work of managers of unease, the staging of highly stylized demonstration projects, and the fabrication of fantasy documents. We focus on how officials emphasize that they have contemplated and planned for all possible security threats, especially catastrophic threats and worst-case scenarios. Actually planning for these events is epistemologically and practically impossible, but saying and showing that authorities are 'planning for the worst' are discursive ways of transforming uncertainty into apparently manageable risks that are independent of the functional activities they describe. As such, our analysis provides insights into the much broader issue of how authorities sustain the appearance of maximum security in order to maintain rhetorical control over what are deemed to be highly uncertain and insecure situations. Such performances may paradoxically amplify uncertainty, thus recreating the conditions that foster the ongoing securitization of everyday life.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".