Minding the Gap: Why or How Nova Scotia Should Enact a New Cyber-Safety Act - Case Comment on Crouch v. Snell
Bibliographic record
Abstract
Nova Scotia’s Cyber-safety Act was meant to fill a gap in the law. Where criminal charges and civil claims like defamation were unavailable or undesirable, the Act, it was hoped, would contain a substantive definition of cyberbullying, set out when it was actionable, and provide procedures for victims to obtain remedies. But the statute that was ultimately passed was too blunt a tool to address the problem, from both a substantive and a procedural perspective.\nThat helps explain why Justice McDougall of the Supreme Court of Nova Scotia struck down the entire statute as unconstitutional, in the recent case of Crouch v. Snell. Now that the Cyber-safety Act is no more, the gap is back. Since the statute was enacted, in 2013, there have been amendments to the Criminal Code and developments in tort law that arguably temper the need for a revised statute.\nSo is there still a gap that needs filling? This case comment suggests that there is, in light of the continued prevalence of harmful online speech — but only if it is filled properly. In filling the gap the second time around, the Legislature should take some cues from Justice McDougall’s decision which, though not perfect, lays the groundwork for what reasonable limits on the substantive definition of ‘‘cyberbullying,” and reasonable tweaks to the process, should look like.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".