Exploiting sport events: Towards a breaking point?
Bibliographic record
Abstract
What would FIFA be without the World Cup, UEFA without the Champions League, or the IOC without the Olympics? Creating and selling high-demand sport events (with related broadcasting and sponsorship deals) has become the main financial asset for the organisations that control the most popular sports. This has turned sport event ownership, management and marketing into a battlefield for increasingly competing actors. International Governing Bodies, National Leagues and private organisations try to maximise their revenues by multiplying fixtures, introducing new events, and diversifying their formats. Football/soccer has recently offered a range of blatant examples of this trend, such as: the failed attempt by a small group of wealthy clubs to break away from the UEFA Champions League and create a European Super League (Brannagan et al. 2022); European top leagues’ refusal to postpone scheduled fixtures despite many teams being decimated by Covid-19 breakdowns; FIFA’s plans to run the World Cup every two years; UEFA and CONMEBOL’s improvised ‘Finalissima’ between the Copa America winners and Euro 2020 winners. Many other sports follow similar trajectories, e.g.: the introduction and rapid success of the Twenty20 format in cricket, with the Indian Premier League’s events overshadowing traditional Test matches (Gupta 2014); the takeover of the Davis Cup by Gerard Pique’s private company Kosmos Tennis; the creation of the International Swimming League by the Russian-Ukrainian billionaire Konstantin Grigorishin; the emergence of competing events and organisations in the field of lifestyle/action sports (Strittmatter et al 2019) and the subcultural tensions related to the co-optation/incorporation of these once alternative practices within mainstream events such as the Olympics (Thorpe & Wheaton 2019). While competing for (media) audiences and calendar slots, these conflicting sport events become contested political arenas for broader power struggles around the governance and ownership of sports at large, including its private or public nature goods (Donnelly 2015; Gammelsæter 2021). As a result, ever growing numbers of fixtures and events are scattered across increasingly congested calendars to cater for the supposedly unlimited ‘hunger’ of sport fans/viewers. This negatively affects the athletes’ wellbeing and performance, and consequently the spectators’ experience, ultimately eroding and deteriorating the ‘product’ on offer. This scenario raises several questions: • How sustainable is this hyper-exploitation of sport events, before the system reaches a breaking point? (e.g. Can athletes maintain high-standard performance within increasingly frequent events, and with what consequences? Which forms of resistance are they displaying/developing? Will fans’ desire for additional events reach saturation? Which forms of resistance are fans displaying, and will those include viewing/attendance boycotts?)
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".