Commercial Gambling and the Surplus for Society
Bibliographic record
Abstract
Background: Gambling is an important source of public revenue in many countries. Little is known about how this revenue is generated, and how it depends on product portfolios, operating costs, turnover, and the institutional contexts of the industry. Methods: A comparative analysis of income statements from 30 European gambling companies is reported. Scatter diagrams are used to describe how the surplus depends on volume, operating costs, monopoly status, and the game portfolio measured by aggregate return-to-players (RTP). Company profiles are used to interpret the results. Hypotheses: Commercialization increases aggregate return to players. This is likely to lower the surplus. Low operating costs of automated and fast games compensate for this loss. Commercial companies produce less surplus than monopolies. Results: The surplus is a linear function of the total revenue. Excluding three big companies, total volume is positively associated with the average return percentages but not proportionately with operating costs. The difference between monopolistic and market-based companies does not appear to be significant. Detailed descriptive analysis shows that the European gambling market may be facing a situation of supply saturation where further growth of gambling proceeds for good causes can no longer be accomplished.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".