Do Social Casino Gamers Migrate to Online Gambling? An Assessment of Migration Rate and Potential Predictors
Bibliographic record
Abstract
Social casino games (i.e., free-to-play online gambling games) are enjoyed by millions of players worldwide on a daily basis. Despite being free to play, social casino games share many similarities to traditional casino games. As such, concerns have been raised as to whether social casino games influences the migration to online gambling among people who have not engaged in such activity (see Griffiths in World Online Gambl 9:12-13, 2010). To date, however, no empirical research has assessed this possibility. Thus, the purpose of the present research was to assess the extent to which social casino gamers migrate to online gambling and potential predictors (time spent on social casino games, skill building, enhancement and micro-transactions) of such migration. To this end, social casino gamers who never gambled online (N = 409) completed a questionnaire battery assessing our variables of interest and were re-contacted 6-months later to see if they had engaged in online gambling during the intervening months. Approximately 26% of social casino gamers reported having migrated to online gambling. Importantly, engagement in micro-transactions was the only unique predictor of migration from social casino gaming to online gambling. The implications for the potential harms associated with social casino gaming 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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".