"Public welfare" factor’s impact on people’s willingness to gamble
Bibliographic record
Abstract
Abstracts Approximately half of total lottery sales are used for prizes, with the remainder being used for government revenue, social welfare endeavors, and distribution fees. 90%, if not more than 95%, of the funds in the casino are returned to the gamblers. China's Welfare Lottery began in 1987, while China's Sports Lottery began in 1983. Data shows that in China, a total of 373.285 billion yuan in lottery tickets were sold nationwide in 2021, an 11.8% increase year on year. Sales of welfare lottery institutions were 142.255 billion yuan, a 1.5% decrease year on year; sales of sports lottery institutions were 231.030 billion yuan, a 21.9% increase year on year. This study aims to determine if more people would try gambling if it were changed to provide more benefits to society (such as building roads in the city or donating to animal shelters with some of the gambling money of those who don't win). Implication statements If the gambling industry invests some of the money people lose in public welfare. Those who believe they have had no success may wish to try again. They believe that even if they lose, they are spending money to benefit society. This increases revenue while keeping guests happier.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".