Bibliographic record
Abstract
The Chinese are known throughout the world as avid gamblers with a long history of participation in games of chance. Historians have documented wagering on such games as far back as the early Chinese dynasties. Despite measures by ancient Chinese rulers to contain gambling, it proliferated, and Chinese games have evolved and multiplied since then. Desmond Lam provides a unique look into the little-known world of Chinese gambling from historical, cultural, psychological, and social perspectives.Chinese gamblers regularly patronize casinos in the United States, Canada, and Australia. The recent expansion of gambling in East Asia has attracted much global media attention. Macau, the only place in China where casino gambling is now legal, easily surpasses Las Vegas as the world's largest casino gaming market. Each year, Chinese from mainland China, Hong Kong, and Taiwan account for almost 90 percent of visitors to Macau.The expansion of the Chinese gambling industry has brought about much harm to Chinese communities, despite all of the development it has also stimulated. This book is the first to examine the beliefs, motivations, attitudes, and behaviors of Chinese gamblers, and will be of interest to students of history and sociology, as well as those studying the history and culture of China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".