Bibliographic record
Abstract
As the internet rapidly evolves and its wide influence expands, the Canadian government (as well as many other nations) have struggled to create and enforce policies that protect people online. This has become especially problematic as digital spaces used by children are constantly growing, and even more so as these children become active participants in not only the consumption but also the creation of internet content, leaving them vulnerable to privacy breaches and labour exploitation. Child Internet stars, or kidfluencers, are a vulnerable group that has relatively no legislative protection. Although the United States has laws to protect child actors from financial exploitation, these laws do not address other forms of abuse or the privacy of these children, and no laws exist in Canada or the United States to protect online child performers from the exploitation of their private lives and labour (Geider 2021 pg. 29). Instead, the responsibility falls to parents to ensure their kids are safe and protected when producing content online, but when the abuse stems from the family, no one is there to protect children from their parents. In the absence of concrete regulation, the onus falls on the platforms themselves to regulate and remove content that exploits children; however, content regulation has its own drawbacks. The Canadian government has the opportunity to act as an international legislative leader by putting forth legislation that requires platforms to cooperate with a national child digital welfare service to ensure fair treatment and compensation for this new generation of internet stars. This paper outlines various issues in regulating child based content as well as suggests possible policy solutions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".