Bibliographic record
Abstract
Online gambling exists on a legal continuum. Currently, several countries prohibit most or all forms of online gambling. This includes Bermuda, Cambodia, China, Cuba, Germany, Greece, India, Malaysia, Romania, South Africa, and the Ukraine. In addition, many (predominantly Islamic) countries ban online gambling by virtue of their ban on all forms of gambling: Afghanistan, Algeria, Bangladesh, Bhutan, Indonesia, Iran, Jordan, Libya, Mali, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, United Arab Emirates, and Yemen (Online Casino Suite, 2011). At the other end are countries that have either completely legalized, or at least permit, all forms of online gambling. These include Antigua and Barbuda, Austria, Gibraltar, Liechtenstein, Netherland Antilles, Panama, the Philippines, Slovakia, and the United Kingdom. In the middle are countries that have put some legal restrictions on it. For example, many countries allow certain forms (most typically online lotteries, instant lotteries, sports betting, horse racing) and make other forms illegal (most typically, casino games). Countries with this policy include Australia, Belgium, Brazil, Canadian provinces, Chile, Czech Republic, Denmark, Finland, France, Hong Kong, Hungary, Iceland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, Macau, the Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Singapore, Slovenia, South Korea, Sweden, Switzerland, Taiwan, and the United States. Several jurisdictions allow participation in online gambling from domestic sites, but prohibit residents from accessing online gambling outside the country. Jurisdictions with this approach include Austria, Belgium, Denmark, Estonia, France, Germany, Hong Kong, Hungary, Israel, Italy, Norway, Slovenia, South Korea, and the United States. Some countries go further to restrict patronage of domestic online sites to residents only (e.g., Austria, Canadian provinces, Finland, the Philippines). Finally, a few countries permit online gambling, but prohibit their own residents from accessing these sites (e.g., Australia for online casinos, Malta, Papua New Guinea).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.012 |
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".