Children’s Privacy and Video Games: Comments on Commercial Surveillance ANPR, R111004
Bibliographic record
Abstract
These comments were prepared in response to the US Federal Trade Commission's advance notice of proposed rulemaking on commercial surveillance and data security (ANPR R111004). In these comments, we examine privacy and surveillance issues in children's media with a focus on video games. We argue in favour of context-sensitive rules for game design and a robust approach to children's privacy that takes into consideration their agency and best interests on a case-by-case basis. Amongst other things, we highlight the importance of the purportedly innocuous nature of play in determining whether a practice is unfair or deceptive and the market realities of smaller game developers who find themselves relying on third-party application libraries from larger companies. We also advocate for protecting children's privacy irrespective of parental consent and adopting a proportionate, risk-based approach to privacy rules, including age-assurance systems.
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 | 0.013 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".