Loot Box State of Play 2023: Law, Regulation, Policy, and Enforcement around the World
Bibliographic record
Abstract
Loot boxes can be bought with real-world money inside video games to obtain random items of varying value. Although these mechanics are gambling-like, they are widely available for purchase, including in children's games. Many countries are considering better regulation. The rapid regulatory and policy developments and proposals across the world in recent years are summarized: (i) probability disclosure requirements in Taiwan, South Korea, and China; (ii) enforcement of gambling law in Belgium, Austria, Finland, the Netherlands, France, the UK, and Australia; (iii) enforcement of EU consumer protection law in Italy, the Netherlands, and the UK; (iv) age ratings and warning labels in Germany, Australia, and the U.S.; (v) expanding the legal definition of “gambling” so as to encompass loot boxes in Finland and Brazil; (vi) the ambitious dedicated regulatory regime in Spain; (vii) class action civil litigation in the U.S. and Canada; (viii) industry self-regulation in the UK; and (ix) attempts to ban online games of chance in India.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".