Focus on fantasy:An overview of fantasy sport consumption
Bibliographic record
Abstract
Fantasy sport is an online activity holding the attention of millions of sport consumers. Industry estimates have fantasy sport being consumed by nearly 35 million US and Canadian participants (Fantasy Sport Trade Association, 2012a).This is an industry quietly becoming a force in the sport communication landscape. Fantasy sport provides consumers with a unique sport encounter aside from traditional ways of consuming sport (that is, viewing, listening, or following a team or sporting event). From statistics to social interaction, there are many factors giving reason as to why people participate in this activity.The fantasy sport user is a unique consumer of sport-based communication and media. These users experience sport beyond team wins, losses, and championships. They become immersed in the minute details and information of sport.They consume statistics as fantasy points, individual players as products, and injury reports as team-altering news. These users view sport through a unique lens. Understanding this type of consumption is important in developing advertising, communication, and marketing campaigns geared towards these consumers. In addition, understanding these consumers provides sport entities with an inside look at what makes this distinct set of consumers unique.The subsequent portions of this chapter provide an overview of the history of fantasy sport and give detail into its consumer motives and consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".