Regulating Childrens' Virtual Worlds to Prevent Harm to Imagination Development: Drawing Lessons from Marketing Regulation
Bibliographic record
Abstract
Close your eyes, relax, take a deep breath. Imagine you are sitting on a beautiful beach with the sound of the waves in your ears. Now open your eyes. What do you see? Nothing, right?! What if you had no imagination?! What if the medium, TV, the internet or virtual worlds stifled your imagination development as kids?! And if so, how can we prevent it, this is the question that this article is trying to answer.The assumption that virtual worlds, the strongest medium up to date, has deleterious affects on imagination development is based on an original theory grounded in the "medium theorists" literature. The medium theorists argue that the form in which people communicate has an impact beyond the choice of specific messages. "the medium is the message" to quote Marshall McLuhan. Virtual worlds are internet based three dimensional environments in which users can play and interact using an avatar, a virtual representation of their choice. One virtual world users has spent 5.93 million years using it. Counting backwards, this is almost exactly the moment in history that our earliest human ancestors first stood upright.In this context, Kline argues that, "Play, the most important modality of childhood learning is thus colonized by marketing objectives making the imagination the organ of corporate desire. The consumption ethos has become the vortex of children's culture".Can we regulate children's virtual worlds in a way that will prevent the harm to their imagination development? Can we do it effectively? And if so how? To answer these question this paper explores the regulatory framework of marketing online in three distinct jurisdictions: Canada, Israel and Australia.Protecting our children’s imagination is a pressing matter that require creative means of regulation. If we fail to protect them today, we might be facing an imaginationless generation tomorrow. The consequences, according to Dubos, might be crucial, "Man's propensity to imagine what does not yet exist, including what will never come to pass…most clearly differentiates him from animals".This paper first outline the assumed harm to the imagination, than review regulation in general and technology regulation in specific. Further the paper explore online marketing to children regulation in three distinct jurisdiction: Canada, Israel and Australia. Finally the paper draw conclusions and offer innovative means of regulation to protect children imagination development in virtual worlds.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".