The gamblers of the future? Migration from loot boxes to gambling in a longitudinal study of young adults
Bibliographic record
Abstract
This repository contains the SPSS datasheet & supporting data information for an upcoming publication: The gamblers of the future? Migration from loot boxes to gambling in a longitudinal study of young adults. Abstract: Cross-sectional studies have established a robust correlational link between loot box engagement and problem gambling, but the causal connections are unknown. This longitudinal study tested for ‘migration’ from loot box use to gambling initiation 6-months later. A sample of gamers (aged 18-26) was stratified into two subgroups at baseline: 415 non-gamblers and 221 gamblers. Self-reported engagement with video game microtransactions distinguished loot boxes and ‘direct purchase’ microtransactions (DPMs). Loot box expenditure and the Risky Loot Box Index (RLI) were tested as predictors of gambling initiation and spend at follow-up. At baseline, gamblers spent significantly more than non-gamblers on microtransactions. Among baseline non-gamblers, loot box expenditure and RLI predicted gambling initiation (logistic regressions) and later gambling spending (linear regressions). DPM expenditure did not predict gambling initiation or spend, underscoring the key role of randomized rewards. Exploratory analyses tested whether baseline gambling predicted loot box consumption (the ‘reverse pathway’): among loot box non-users, gambling-related cognitive distortions predicted subsequent loot box expenditure. These data provide empirical evidence for a migration from loot boxes to gambling. Preliminary evidence is also provided for a reverse pathway, of loot box initiation by gamblers. These findings support regulatory steps directed toward young gamers and those who gamble.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".