Law as an Ally or Enemy in the War on Cyberbullying: Exploring the Contested Terrain of Privacy and Other Legal Concepts in the Age of Technology and Social Media
Bibliographic record
Abstract
This article focuses on the role and limits of law as a response to cyberbullying. The problem of cyberbullying engages many of our most fundamental legal concepts and provides an interesting case study. Even when there is general agreement that the problem merits a legal response, there are significant debates about what that response should be. Which level and what branch of government can and should best respond? What is the most appropriate legal process for pursuing cyberbullies—traditional legal avenues or more creative restorative approaches? How should the rights and responsibilities of perpetrators, victims and even bystanders be balanced? Among the key legal concepts that will be explored are privacy, free speech, liberty, and equality. These are the cornerstones of Canada’s constitutional framework and striking the proper balance between them is a challenging and complex business.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".