“Just Being Mean to Somebody Isn’t a Police Matter”: Police Perspectives on Policing Cyberbullying
Bibliographic record
Abstract
Increasing public awareness of cyberbullying, coupled with several highly publicized youth suicides linked to electronic bullying, have led lawmakers and politicians to consider new criminal legislation specifically related to cyberbullying. However, little is known about how the police currently respond to cyberbullying, and it is not clear whether new laws are necessary. In this article, the authors draw upon in-depth interviews with Canadian street patrol officers and school resource officers to explore police perspectives on policing cyberbullying. In contrast to the reactive hard-line approach proposed in much legislation and public discussion, police officers prefer to take a preventative approach by educating youth and raising awareness about the dangers of digital communications. Although there are instances when criminal charges must be laid, these incidents transcend “bullying,” a term that has little legal meaning for police officers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".