Examining Children’s Applications of Privacy Norms in a Digital, Photo-Sharing Game
Bibliographic record
Abstract
Children develop an understanding of privacy through experiences in both real and virtual contexts. As technology becomes central to their lives, it is crucial to explore how they navigate privacy in digital environments. This study examined children’s application of privacy norms in a digital photo-sharing context and tested whether a privacy intervention could improve their understanding. In Experiment 1, 85 children (ages 5–8 years) and in Experiment 2, 35 children (ages 5–7 years) listened to a story about Sally, who appeared as a cartoon and a real person. Children decided whether Sally should allow a game to take her picture in different settings. Older children judged taking real Sally’s picture as less permissible than cartoon Sally’s. Experiment 2 introduced a privacy intervention, which influenced judgments equally across both versions of Sally. These findings suggest that children’s privacy reasoning develops with age and can be shaped by targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".