MétaCan
Menu
Back to cohort
Record W2223812292 · doi:10.11575/prism/9581

Gambling in Canada : special report : video lottery terminals in New Brunswick

2001· article· en· W2223812292 on OpenAlexaboutno aff
Jason J. Azmier

Bibliographic record

VenueOpen MIND · 2001
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsLotteryComputer scienceTelecommunicationsMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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.

Opus teacher head0.170
GPT teacher head0.418
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueOpen MINDSame topicGambling Behavior and TreatmentsFrench-language works237,207