Children’s digital playgrounds as data assemblages: Problematics of privacy, personalization, and promotional culture
Bibliographic record
Abstract
Children’s digital playgrounds have evolved from commercialized digital spaces such as websites and games to include an array of convergent digital media consisting of social media platforms, mobile apps, and the internet of toys. In these digital spaces, children’s data is shared with companies for analytics, personalization, and advertising. This article describes children’s digital playgrounds as a data assemblage involving commercial surveillance of children, ages 3–12. The privacy sweep is used as a method to follow the personal information traces that can be expected to be disclosed through typical use of two children’s digital playgrounds: the YouTube Kids app and Fisher-Price Smart Toy plush animal and companion app. To trace the data flows, privacy policies and other publicly available documents were analyzed using political economy and privacy informed indicators. This article concludes by reflecting upon the dataveillance and commercialization practices that trouble the privacy rights of the child and parent when data assemblages in children’s digital playgrounds are surveillant.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".