Gambling in Canada : special report : video lottery terminals in New Brunswick
Bibliographic record
Abstract
New Brunswick is the birthplace of Video Lottery Terminals (VLTs) in Canada.VLTs were first introduced into New Brunswick in 1990 after a 1985 Criminal Code amendment permitted provinces to operate electronic gaming machines.All provinces would eventually follow New Brunswick's lead and embrace electronic gambling through slot machines, VLTs, electronic bingo, satellite bingo and electronic keno.This report provides an overview of the development of VLT policy over the last dozen years and the current extent of VLT gambling in Canada.VLTs are a unique form of gambling, different from other gambling in a number of ways.First, instead of coins, VLTs use "credits" that can not be redeemed until cashed-in elsewhere on the premises.This has the effect of psychologically separating the player from the amount won/lost or wagered.Second, VLTs operate much quicker than most forms of gambling, including many slot machines.This allows for more plays in single session, instant gratification and rapid wins or losses.Third, VLTs are more accessible.They are found in bars and lounges (traditional non-gambling venues), which increases the likelihood of casual play and exposes gambling to new audiences.Finally, video lottery is a relatively easy game to play.Virtually anyone can quickly learn to gamble on these machines without requiring any special skill.Together this combination presents a number of policy challenges that differentiate VLT gambling as a controversial form of gambling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".