Organizing and conducting sporting events online : a study of the 2011 CrossFit Games
Bibliographic record
Abstract
In a world where everything from dating to shopping to conducting business can be performed\nonline, competitive sport has remained an activity in which its online component is mostly relegated to news,\nchat or fan forums, or fantasy-league interactions. The physicality of competitive sports does not lend itself\nto an online format—until now. CrossFit (CF) is one of the fastest growing new fitness programs in the\nworld, and is based largely on online communities and networks. In 2011, CrossFit Incorporated (CF Inc.),\nthe creator of this worldwide fitness network, conducted the world’s largest online CF sporting competition,\nwhere individuals recorded their performances online for public consumption, interaction and judging. Over\n25,000 individuals and teams from around the world participated in 2011 which relies heavily on\nparticipation and feedback, trust, social media and networking for its success.\nBy uncovering the essential components of the unique operating community of CF through analysis\nof quantitative data, in-depth qualitative interviews, and textual analysis, this paper suggests a model for\nproducing a successful global sporting event online and discusses whether it may be applied to other athletic\norganizations to increase their worldwide exposure and increase members access to global opportunities.\nFindings determined a mix of criteria including attracting and retaining like-minded individuals through a\nstrong focus on cohesion, inclusion, and competition; strong local autonomy and control; and a willingness\non the part of members to promote the sport for the perceived wellbeing of others as necessary to a strong,\neffective online component in facilitating global competition online.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".