The Dark Side of Authenticity: Feeling “Real” While Gambling Interacts with Enhancement Motives to Predict Problematic Gambling Behavior
Bibliographic record
Abstract
Engaging in activities that make people feel authentic or real is typically associated with a host of positive psychological and physiological outcomes (i.e., being authentic serves to increase well-being). In the current study, we tested the idea that authenticity might have a dark side among people engaged in an addictive or risky behavior (gambling). To test this possibility, we assessed gamblers (N = 61) who were betting on the National Hockey League playoff games at a sports bar. As predicted, people who felt authentic when gambling reported behavior associated with problem gambling (high frequency of betting) as well as problematic play (a big monetary loss and a big monetary win). Moreover, such behavior and gambling outcomes were particularly high among people who were motivated to gamble for the purpose of enhancement. The interaction of feeling authentic when betting and gambling for purposes of enhancing positive emotions proved especially troublesome for problematic forms of play. Implications of authenticity as a potential vulnerability factor for sports betting and other types of gambling are discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".