Journal of Military and Strategic Studies Olympic Security: Assessing the Risk of Terrorism
Bibliographic record
Abstract
sixteen days of athletic competition, international revelry and an opportunity to showcase Canada on the world stage. This last benefit, however, is a double-edged sword because the Games run the risk of being overshadowed by negative events as well. Historically, the Olympic Games have always served as a platform for drawing attention to specific political grievances. They have been used as “a vehicle to embarrass host governments, draw attention to injustices, apply political blackmail and raise serious ethical concerns ” 1 on many occasions. Traditionally, these concerns have manifested themselves in social and political demonstrations: drawing attention to civil rights issues (Tommie Smith and John Carlos “power to the people ” salute during Mexico 1968 Games), minority issues (treatment of Aboriginals during Sydney 2000 Games), and human rights issues (the crackdown on Tibetan protesters during the Beijing 2008 Games). However, the Olympic Games have also fallen victim to episodes of terrorism, most notably the kidnapping and execution of Israeli athletes by Black September during the 1972 Munich Games and the Centennial bombing at the 1996 Atlanta Games. In fact, between 1972 and 2004, there have been 168 terrorist attacks related to sporting events more generally. 2 This paper explains the very real security issues which Canadian Olympic organizers will face this February. It documents the traditional security challenges facing all Olympic organizers, especially those responsible for Games occurring post
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".