Internet Poker: Examining Motivations, Behaviors, Outcomes, and Player Traits using Structural Equations Analysis
Bibliographic record
Abstract
Hypotheses explaining outcomes from internet poker were tested by using structural equations modeling: Personal characteristics and traits were proposed to influence motivation, leading to gaming behavior and then to outcomes. One hundred ninety-four participants from an internet poker forum completed online assessment. Three separate outcomes were supported: social-emotional gains, monetary winnings/losses, and negative experiences. One third of the participants reported some negative outcomes and 12% said these were significant; two thirds indicated no negative outcomes. Problems were most linked to the trait of Neuroticism, younger age, and more hours played, but unrelated to amounts won or lost. Gaming for social-emotional benefits mediated fewer negative outcomes. Financial gain motivation was a key mediator for gaming behavior. Findings were consistent with research showing negative emotionality and youth to be associated with poor gambling outcomes. The model suggests concrete actions that can be taken to minimize problem gaming while maximizing healthier involvement with online poker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".