Illegal video game loot boxes with transferable content on Steam: a longitudinal study on their presence and non-compliance with and non-enforcement of gambling law
Bibliographic record
Abstract
Loot boxes are gambling-like mechanics in video games that can be bought with real-world money to obtain random rewards. Regulators in many countries have considered whether different loot box implementations fall within the existing legal definition of ‘gambling.’ Most countries’ regulators say that only loot boxes (i) players spent real-world money to purchase and (ii) provide randomised content (iii) that possesses real-world monetary value (e.g., be transferable between players) legally constitute ‘gambling.’ A comprehensive review of Valve’s Steam platform for PC games identified 35 games, including some of the most popular games with hundreds of thousands of concurrent players, that implement paid loot boxes with transferable content worth real-world money. These would likely fall afoul of current gambling laws in many countries. Contrary to previous statements published by gambling regulators promising enforcement, consumers, policymakers, and other stakeholders should be aware of the existence and popularity of these presumably illegal loot boxes and how gambling law has not actually been enforced against them. The situation remained unchanged one year later. This longitudinal perspective demonstrates a continued state of non-compliance by game companies and non-enforcement by gambling regulators that leaves consumers unprotected and at risk of encountering illegal content and experiencing harm.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".