Cyber victimization and social cohesion: Unraveling correlates of cyberbullying and cyberstalking in Canada
Bibliographic record
Abstract
This study leverages data from the Canadian General Social Survey, Cycle 34, to explore factors linked to cyberbullying and cyberstalking victimization over the past five years. Using theoretical frameworks such as social cohesion, social support, social disorganization , and routine activities, the research identifies variables associated with increased cyber victimization risk. The findings reveal both parallels and distinctions between cyberspace and physical space in the application of these theories. Higher levels of internet use, experiences of discrimination, concerns about personal safety, fewer close social ties, and unmarried status are linked to greater vulnerability to cyberbullying and cyberstalking. However, neighborhood contexts—such as community social support and collective efficacy—appear unrelated to cyber victimization. This research sheds light on the unique dynamics of cybervictimization and provides critical guidance for policymakers to inform targeted prevention and intervention strategies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".