To screen, or not to screen: An experimental comparison of two methods for correlating video game loot box expenditure and problem gambling severity
Bibliographic record
Abstract
Loot boxes are gambling-like products found in video games that players can buy with real-world money to obtain random rewards. A positive correlation between loot box spending and problem gambling severity has been well-replicated. Some researchers recently argued that this observed positive correlation may be due to participants incorrectly interpreting problem gambling questions as applying to their loot box expenditure because they see loot box purchasing as a form of ‘gambling.’ We experimentally tested this alternative explanation for the observed positive correlation (N = 2,027), by manipulating whether all participants were given the problem gambling scale as the previous literature generally had (the ‘non-screening’ approach; n = 1,005), or by ‘screening’ participants (n = 1,022) by only giving the problem gambling scale to those reporting recent gambling expenditure. Through the latter screening process, we clarified and calibrated what ‘gambling’ means by providing an exhaustive list of activities that should be accounted for and specifically instructed participants that loot box purchasing is not to be considered a form of ‘gambling.’ Results showed positive correlations between loot box spending and problem gambling across both experimental conditions. In addition, a predicted positive correlation emerged between binary past-year gambling participation and loot box expenditure in the screening group. These experimental results confirm that the association between loot box spending and problem gambling severity is likely not due to participants misinterpreting problem gambling questions as being relevant to their loot box spending. However, problem gambling severity was inflated in the non-screening group, meaning that future research on gambling-like products should include gambling participation screening questions; better define what ‘gambling’ means; potentially exclude non-gamblers from analysis; and, importantly, provide explicit instructions on whether certain activities should not be considered a form of ‘gambling.’
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".