How the Covid-19 Pandemic Influenced the Approach to Risk Management in Cycling Events
Bibliographic record
Abstract
The COVID-19 pandemic has taught us to live in social isolation and has brought an important element of social life, the events industry, to a complete standstill. In resurrecting the events industry, the most urgent focus is on managing the risk of any crowd-control measures with a view to reducing to zero the danger of the virus spreading. This research focuses on the main issue of the impact of the coronavirus disease 2019 (COVID-19) on the organization of sports events (SEs), and in particular, cycling competitions. This study, therefore, aims to provide deeper insights into (a) the measures introduced to face the health emergency situation in cycling events, (b) the comparison of these measures with previous experiences in similar SE contexts, and (c) the possible evolution of organizational models for cycling events in the post-pandemic era. Fifteen semi-structured interviews with cycling athletes, managers, and officials constitute the methodological basis for this study. The results show that countermeasures have been taken that are effective in dealing with pandemic characteristics and are likely to be applied in the future, while others will be phased out or used again only when necessary. This study enhances scientific knowledge by analyzing a renewed approach to risk management for SEs, with a specific focus on pandemics and medical risks. Finally, the study shows that cycling events need to adapt the specifics of such a new approach to the standards projected on future scenarios for which the COVID-19 pandemic has paved the way.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".