Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it