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Record W2910915131

Risk management at public events: a case study of a municipality within Southern Ontario

2018· dissertation· en· W2910915131 on OpenAlexaboutno aff
Rachael Nunes

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental planningPublic managementEnvironmental resource managementPolitical sciencePublic administrationEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

Current literature surrounding risk management at public events focuses on the efforts of municipal officials to reduce the risk of terrorist activity. The literature only focuses on large municipalities that host global sporting events like the FIFA World Cup and the Olympics. The focus tends to be on a broad global view of terrorism superseding other types of mundane criminal activities that are more likely to occur at smaller municipalities and venues. In this thesis, an analysis of potential risks at public events hosted by a medium-size municipality is examined. By analyzing the responses of in-depth interviews with municipal officials, performing a content analysis of their Standard Operating Procedures (SOPs), and participating in direct observation of municipal events, this study determines that differences exist between the risks outlined in existing literature and the perceptions of risk garnered from the experience of those that work in the field. Security officials of this medium-size municipality define risks as ???emergencies??? and consider the risk of stampedes and environmental disasters as a greater threat than terrorism within their 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.256
Teacher spread0.234 · 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.

Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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