Optimal Public Policy for Government-Operated Gambling
Bibliographic record
Abstract
This paper presents a framework for formulating the optimal public policy for government-operated gambling. The goal of public policy with respect to government-operated gambling is typically stated as “harm minimization.” This claim masks the possible trade-off between an increase in social harm (H) and the government’s incremental net revenue (R) from increased gambling activity. Using a graphical approach, we depict first the feasible combinations of H and R, and then identify the combinations that could be classified as efficient, thereby allowing the minimum social harm for any given level of the government’s incremental net revenue from gambling. We indicate how the optimal combination of H and R could be identified and realized in both the short and long run. We then utilize the body of research on gambling and its effects to qualify what this trade-off operates in the real world.RésuméCe document présente un cadre pour l’élaboration d’une politique publique optimale encadrant les jeux de hasard gérés par le gouvernement. L’objectif d’une politique publique en matière de jeu géré par le gouvernement vise généralement une « minimisation des méfaits ». Cela permet de dissimuler le compromis possible entre une augmentation des dommages sociaux (H) et le revenu net supplémentaire du gouvernement (R) provenant d’une augmentation des activités de jeu. À l’aide d’une approche graphique, nous décrivons d’abord les combinaisons possibles entre les méfaits sociaux et les revenus (H et R), puis nous identifions les combinaisons « efficaces », ce qui permet de réduire au minimum les méfaits pour la société à tout niveau des revenus nets supplémentaires tirés du jeu par le gouvernement. Nous indiquons comment la combinaison optimale méfaits/revenus pourrait être établie et réalisée à court et à long terme. Nous utilisons ensuite le corpus de recherches sur le jeu et ses effets pour qualifier à quoi ressemble ce compromis dans le monde réel.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".