Bibliographic record
Abstract
The considerations that apply to the management and protection of children's online privacy are unique and complex. Their still-evolving maturity and lack of experience, coupled with the consequences of permanent online records of youthful actions which can stigmatize into adulthood, make children an especially vulnerable segment of the population. Children's privacy is further contextualized by the United Nations Convention on the Rights of the Child, which calls upon states to 'respect and ensure the rights of children, including the right to the protection of their privacy'. This chapter examines the ways in which key jurisdictions have responded to the special privacy needs of children. In particular, we map the emergence of children's privacy as a trade issue in the United States and outline the provisions of the Children's Online Privacy Protection Act. We contrast the child-specific approach taken in the US with the application of general private-sector data protection principles to children's privacy issues in Canada and Australia. We then explore the transition in the European Union from general protection to child-specific provisions, and the ways in which the European commitment to privacy as both a human right and a child's right have shaped existing regulations as well as the newly enacted General Data Protection Regulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".