COVID 19 - Mass Gathering Events and Attendee Safety Protocol for Turbulent times
Bibliographic record
Abstract
This paper forwards a psychosocial perspective of event safety management. In a time of social and economic upheaval - exasperated by the Covid-19 pandemic, safety has become increasingly significant in the vocabulary of researchers, managers, funding agencies and public bodies involved in the provision of events. The work documented here appraises the role of those with responsibility in managing people and their safety at mass gathering events, i.e. police, event managers and medical teams. It does so through a focus group review process with members from each of the aforementioned groups, and with reference to the analytical frame of the Ottawa Charter (1986). The work contributes to knowledge of event and crowd management, advancing the concept of transformative civic responsibilities while reframing aspects of the event experience to encourage trust and resilience in both attendees and host populations. The work here acknowledges the importance of mass gathering events (MGEs) at a time of social-economic upheaval following Covid-19.
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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 | 0.097 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.045 |
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".