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Record W2042295729 · doi:10.1177/1043986200016004006

Do Casinos Attract Criminals?

2000· article· en· W2042295729 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Contemporary Criminal Justice · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsMinistère de l’Immigration, de la Francisation et de l’Intégration
Fundersnot available
KeywordsCriminologyAdvertisingCrime rateViolent crimeBusinessPolitical scienceDemographic economicsLawPsychologyEconomics

Abstract

fetched live from OpenAlex

This study examines trends in the number of criminally inadmissible persons who seek admission to Canada because of the opening of Casino Niagara. The purpose of this study is to determine whether the presence of a new Canadian casino increases the rate at which criminally inadmissible persons seek entry to Canada. The impact of the casino is assessed by examination of trends in total bridge crossings before and after the opening of the casino, trends in the total number of criminals denied entry to Canada, and trends in the proportion of criminals who have convictions for offenses related to organized crime. The rate of denials of entry to Canada on grounds of criminal inadmissibility rose faster than did the border traffic in general, but there was a decline in the proportion of those with prior records for organized crime-related offenses. The implications of these findings are presented.

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 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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.352
Teacher spread0.267 · 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