Leading Through Crisis: Competencies for Effective Sport Security Professionals
Bibliographic record
Abstract
Professional sporting events represent an increasingly growing segment of the national economy and, as a pastime, include annual participation from hundreds of millions of spectators. Providing effective safety and security for these events is a daunting task. Many professional sport venues are iconic structures for mass gatherings that represent susceptible targets for crises such as rising episodes of fan violence, natural disasters, and acts of terrorism. As concerns are ongoing, professional sport organizations need security professionals who not only have the competencies to manage a crisis, but who also lead an organization post-crisis in order to affect organizational learning and improvement. A combination of crisis management and crisis leadership competencies has been developed through this research and form the dependent variables of the newly formed Crisis Readiness Score (CRS) research instrument. The study documents and establishes a baseline for the perceived levels of these crisis readiness competencies. Through hypothesis testing, the study also examines the relationships between education levels, experience levels, and participation in training on the crisis readiness competencies. The study targeted individuals responsible for security at six major professional sport venues throughout the United States and Canada. The questionnaire was sent to 151 security directors with 71 of the surveys completed. A statistical multiple regression was performed to analyze the hypotheses. Education level was not found to be a significant predictor of crisis readiness competency development. Both experience level and participation in training were found to be significant predictors of crisis readiness competency development. The study enhances previous collegiate sport security research by identifying the level of competencies held by the professional sport security workforce. The findings also establish a baseline to which subsequent measures of such competencies can be compared.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".