Internet Poker Gambling Among University Students: A Risky Endeavour or a Harmless Pastime?
Bibliographic record
Abstract
Two recent phenomena have marked gambling on university campuses: an increase of Internet gambling and a surge of interest in poker (McComb & Hanson, 2009). Accompanying them, greater participation and problem gambling rates among university students have been observed (Griffiths & Barnes, 2008; Wood et al., 2007). This thesis aims to describe online poker gambling patterns and the associated risks among university students, and to determine if the Internet as a context is linked to a greater risk of problematic and excessive gambling engagement and related problems. It compares online to offline poker players. The sample (N=1,256) was drawn from the University Student Gambling Habit Survey 2008 (ENHJEU) conducted among undergraduate students in three universities and three affiliated schools in Montreal, Canada. The analyses revealed that compared to offline poker players online poker players were more likely to be male and born outside of Canada. Their gambling patterns also suggested greater gambling engagement. Online poker players were much more likely than offline poker players to be identified as problem gamblers and to report problems in various major life areas. Virtually no differences were found in co-occurring risky behaviours, such as smoking, alcohol and substance use between the two groups. The findings point to an increased risk for gambling and other problems associated with the Internet and poker gambling for university students. Discussed are potential reasons including the enabling nature of the Internet setting with respect to gambling, as well as the prevailing perception of poker as a skill-based gambling format.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".