Opening the compliance and enforcement loot box: A retrospective on some practice and policy impacts achieved through academic research
Bibliographic record
Abstract
Loot boxes are gambling-like products in video games that can be purchased with real-world money to obtain random rewards. Regulations have been imposed in some jurisdictions to attempt to address potential harms. Two recent policy studies assessed companies’ compliance (but more often, non-compliance) with those regulations. The first study found that a supposed ‘ban’ on loot boxes in Belgium was not enforced so the product remained widely accessible. A preprint reporting this was widely publicised by the media. This enhanced awareness led to companies newly complying with the law and helped policymakers to view the practicality of banning loot boxes with more due scepticism. Researchers should consider actively sharing non-peer-reviewed preprint results to protect consumers more promptly. The second study found that, contrary to regulations, many games with loot boxes were not labelled. Subsequent engagement with the media and the industry self-regulators caused remedial actions to be taken: unlabelled games have since been correctly labelled, and non-compliant companies have been punished with (albeit insignificant) fines. The societal impacts of loot box policy studies demonstrate the importance of actively communicating research results to the public through media engagement and challenging companies and regulators when they are not complying with or enforcing regulations.
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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 | 0.088 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".