COPPAcalypse? The YouTube Settlement’s Impact on Kids’ Content
Bibliographic record
Abstract
We examine how privacy restrictions affect online content creation and consumption by evaluating the impact of YouTube's settlement with the Federal Trade Commission over violating the Children's Online Privacy Protection Act (COPPA). Under the settlement, YouTube limited personalization for made-for-kid (MFK) content starting in January 2020, which included personalized ads and content engagement features like subscriber notifications and playlists. We study the resulting impact on 5,066 top American YouTube channels by comparing the MFK content creators to their non-MFK counterparts using a difference-in-differences design. On the supply side, MFK content creators produce 18% less content and pivot toward non-MFK content production. MFK content creators also invest less in content quality: the proportion of original content falls by 9% and manual captioning drops by 28%, while viewer content ratings fall by 9%. On the demand side, views of MFK channels fall by 20%. The restrictions also affected market competition, increasing concentration of both content creation and viewership among top MFK channels.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".