Online Safety for Children and Youth under the 4Cs Framework—A Focus on Digital Policies in Australia, Canada, and the UK
Bibliographic record
Abstract
This study analyzes the previous literature on the online safety of children and youth under "the 4Cs risk framework" concerning contact, content, conduct, and contract risks. It then conducts a comparative study of Australia, Canada, and the UK, comparing their institutions, governance, and government-led programs. Relevant research in Childhood Education Studies is insufficient both in quantity and quality. To minimize the four major online risks for children and youth in cyberspace, it is necessary to maintain a regulatory approach to the online exposure of children under the age of 13. Moreover, the global society should respond together to these online risks with "multi-level" policymaking under a "multi-stakeholder approach". At the international level, multilateral discussion within the OECD and under UN subsidiaries should continue to lead international cooperation. At the domestic level, a special agency in charge of online safety for children and youth should be established in each country, encompassing all relevant stakeholders, including educators and digital firms. At the school and family levels, both parents and teachers need to work together in facilitating digital literacy education, providing proper guidelines for the online activities of children and youth, and helping them to become more satisfied and productive users in the digital era.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".