Coming Out to Play: Privacy, Data Protection, Children’s Health, and COVID-19 Research
Bibliographic record
Abstract
The COVID-19 pandemic has underscored the need for new ways of thinking about data protection. This is especially so in the case of health research with children. The responsible use of children’s data plays a key role in promoting children’s well-being and securing their right to health and to privacy. In this article, we contend that a contextual approach that appropriately balances children’s legal and moral rights and interests is needed when thinking about data protection issues with children. We examine three issues in health research through a child-focused lens: consent to data processing, data retention, and data protection impact assessments. We show that these issues present distinctive concerns for children and that theGeneral Data Protection Regulationprovides few bright-line rules. We contend that there is an opportunity for creative approaches to children’s data protection when child-specific principles, such as the best interests of the child and the child’s right to be heard, are put into dialogue with the structure and logic of data protection law.
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 | 0.111 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.120 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".