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Record W7099867845

Journal of Military and Strategic Studies Olympic Security: Assessing the Risk of Terrorism

2013· article· en· W7099867845 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhysics and Engineering Research Articles
Canadian institutionsnot available
Fundersnot available
KeywordsTerrorismAtlantaPoliticsCentennialAmbush marketingSWORDBeijing
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.271
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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