Who Bets on Sports? Characteristics of Sports Bettors and the Consequences of Expanding Sports Betting Opportunities/¿Quién apuesta? Características de los apostantes deportivos y consecuencias de la expansión de las oportunidades de apostar
Bibliographic record
Abstract
Currently, several proposed changes in sports betting laws are being debated in the United States and the European Union. This article examines the characteristics of sports bettors in three countries, Canada, Spain, and the United Kingdom, to determine who bets on sports in environments where this activity is both legal and popular. Uncondi¬tional and conditional analyses find that annual participation rates in sport betting are low, and that sport bettors tend to be young males with relatively high incomes. Sports bettors stand to gain the most from an expansion of legal sports betting opportunities, while the negative impacts of increased access to sports betting are expected to be mini¬mal in the United States and difficult to assess in the European Union. Actualmente, un importante número de cambios en la legislación del mercado de apuestas deportivas están siendo debatidos tanto en Estados Unidos como en la Unión Europea. En este artículo se examinan las características socio-económicas de los apostantes deportivos en Canadá, España y el Reino Unido con el objeto de determinar el perfil de estos jugadores en contextos donde está actividad es legal y muy popular. El análisis empírico muestra que la fre¬cuencia de participación en este mercado es baja y que el perfil de los apostantes tiende a ser el de un hombre joven con ingresos relativamente altos. Se concluye que los propios apostantes serían los potenciales beneficiarios de una expansión de las oportunidades de apostar, mientras que el impacto negativo de esta liberalización se espera sea mínimo en Estados Unidos y difícil de calcular en la Unión Europea.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".