Cyberbullying: Is Federal Criminal Legislation the Solution?
Bibliographic record
Abstract
Cyberbullying occurs frequently and is often a reciprocal conflict, with individual youths filling the roles of both the victim and the bully in a short period of time. Regardless of the role, involvement in cyberbullying is associated with negative outcomes and has recently been linked to the death of young people in a few cases. In an attempt to alleviate growing concerns about cyberbullying, the Canadian government passed Bill C-13 (the Protecting Canadians from Online Crime Act), which includes a prohibition on the posting of non-consensual intimate images. Due to the specific criteria in this section of the bill, it is unlikely that it will protect many youth from online victimization. Bill C-13 also criminalizes harassing or annoying behaviour conducted via electronic communication. This law may exacerbate the problem of non-disclosure, may be confusing to youth, and may result in too many youth and a disproportionate number of marginalized youth becoming involved in the criminal justice system. Alternative approaches to dealing with the conflict, such as increasing the use of empirically based programs that teach youth to resolve interpersonal conflict and encourage them to disclose incidents of cyberbullying, would be more effective than federal criminal legislation at protecting young people from online victimization.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.028 | 0.021 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".