Preparing for Euro 2012: Developing a Hazard Risk Assessment
Bibliographic record
Abstract
INTRODUCTION: Risk assessment is a vital step in the disaster-preparedness continuum as it is the foundation of subsequent phases, including mitigation, response, and recovery. HYPOTHESIS/PROBLEM: To develop a risk assessment tool geared specifically towards the Union of European Football Associations (UEFA) Euro 2012. METHODS: In partnership with the Donetsk National Medical University, Donetsk Research and Development Institute of Traumatology and Orthopedics, Donetsk Regional Public Health Administration, and the Ministry of Emergency of Ukraine, a table-based tool was created, which, based on historical evidence, identifies relevant potential threats, evaluates their impacts and likelihoods on graded scales based on previous available data, identifies potential mitigating shortcomings, and recommends further mitigation measures. RESULTS: This risk assessment tool has been applied in the vulnerability-assessment-phase of the UEFA Euro 2012. Twenty-three sub-types of potential hazards were identified and analyzed. Ten specific hazards were recognized as likely to very likely to occur, including natural disasters, bombing and blast events, road traffic collisions, and disorderly conduct. Preventative measures, such as increased stadium security and zero tolerance for impaired driving, were recommended. Mitigating factors were suggested, including clear, incident-specific preparedness plans and enhanced inter-agency communication. CONCLUSION: This hazard risk assessment tool is a simple aid in vulnerability assessment, essential for disaster preparedness and response, and may be applied broadly to future international events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".