The Child Labor in Social Media: Kidfluencers, Ethics of Care, and Exploitation
Bibliographic record
Abstract
Kidfluencing, a social media business in which children serve as primary influencers of audience opinions or behavior, is a rapidly growing entrepreneurial phenomenon where parents build enterprises around the likability and antics of their children. Proponents argue that kidfluencing is simply monetizing the existing antics of kids, critics argue that it is child labor. We explore the ethical implications of kidfluencing through the abductive lens of four leading kidfluencer cases—Ryan’s World, Vlad and Nicki, Ninja Kidz, and The Bucket List Family—in light of the United Nations Convention on the Rights of the Child. Through a comparative case analysis of these four highly successful kidfluencing channels, we demonstrate both the ethical challenges as well as the trade-offs between rewards and rights risks within kidfluencing. We develop a model of five fundamental threats to the rights and freedoms of children involved in social media influencing, proposing a framework by which parents, platforms, and policy-makers can assess and regulate this industry. In so doing, we make contributions to the literatures of child labor and the ethics of care.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".