Managing sport and leisure in the era of Covid-19
Bibliographic record
Abstract
In December 2019 the world was first informed of a new virus, called SARS-CoV-2 (hereafter Covid-19) spreading fast originally in China and quickly in the rest of the world, resulting in the hospitalisation and death of millions of people worldwide (World Health Organisation, 2020). The quick and almost unstoppable spread of the virus called for Governments to enact various levels of measures. Whilst these measures were taken to different degrees and time-points, they generally included the introduction of social distancing and lockdown procedures in numerous countries around the globe. As part of these procedures, work and social gatherings were brought to an abrupt halt, disrupting the operations and norms in numerous industries, including the wider sport and leisure industry. From the postponement of mega-sport events, to the stopping of sport leagues, and the closure of leisure centres, the sport and leisure industry adhered to the various Covid-19 measures taken by local and national governments, following the guidance of experts such as the World Health Organisation (World Health Organisation, 2020). As the spread of the virus and our ability to respond to it progressed, these measures changed since they were initially put in place in the spring of 2020, with some forms of more laxed measures in place until the autumn of 2021 when this editorial is being written.
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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 | 0.003 | 0.000 |
| 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".