Impact of Fantasy Sports on Participantsâ Interest in Real-League Occurrences
Bibliographic record
Abstract
Fantasy sports have developed into their own significant section of the sport industry, accounting for $4 billion and having over 32 million participants in the United States and Canada (FSTA, 2012). Academic research into fantasy sports is a fairly new, and much of it has focused on motivation behind fantasy gaming, while any consumer behavior research has been focused on media consumption and team identification. This research is for determining what the relationship between fantasy sport participation and consumption of information about league current events, such as rule changes, labor issues, and team rebranding, is. The participants in this research took a survey posted on Facebook and Twitter, and were between 18 and 25 years of age. This information helps leagues find if fantasy sports help increase the depth of a fanâs commitment to the league. There was also information gathered about fantasy sportsâ perception as a form of gambling, due its tumultuous past.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".