Children’s Understanding of Digital Tracking and Digital Privacy
Bibliographic record
Abstract
Abstract Digital technologies provide both interpersonal and institutional risks for users, and children are particularly vulnerable, given their limited understanding of these risks. This chapter reviews the current state of literature on children’s understanding of technology as it relates to digital privacy and tracking, focusing especially on children under 14 years of age and their parents. Existing work paints a complex picture of children’s privacy representations wherein children show sophisticated thinking regarding some aspects of privacy and tracking (including types of information, interpersonal privacy risks, and benefits of tracking), but also struggle to represent other privacy concepts such as institutional privacy risks, how/when data is accessed, and the diverse entities that might access it. Directions for future research include a greater focus on children younger than 12 years of age, developing and testing effective educational interventions for children and their parents, and research informing child-appropriate design of privacy information and controls. Recommendations include a research-informed consideration of child age in regulatory requirements, practicing privacy by design in addition to legal compliance, introducing meaningful education at earlier ages, and improving transparency regarding data collection in trusted contexts, such as healthcare and educational settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".