Bibliographic record
Abstract
Despite the implementation of numerous policies to combat cyberbullying, its prevalence remains a significant concern. This qualitative study examines the organizational capacity of schools in dealing with cyberbullying. Interviews were conducted with various stakeholders in 22 public and Catholic schools across Ontario, Canada, including principals, vice-principals, social workers, police officers, parents, and students. The study aimed to assess the efficacy of school initiatives and policy tools and determine the extent to which schools possess the capacity to address cyberbullying effectively.Findings revealed that among the 22 schools studied, seven were deemed to have unhealthy climates and lacked the necessary capacity to tackle cyberbullying. All schools were reactive, responding only after incidents were reported, and held the misconception that an absence of reporting indicated the absence of bullying. Moreover, all schools demonstrated some degree of organizational capacity deficit. In the weakest schools, staff exhibited indifference towards cyberbullying, allowing it to persist. Most schools engaged in symbolic actions, such as organizing Pink Shirt Days and Anti-Bullying weeks, without implementing standardized, evidence-based approaches to prevention and intervention. Although schools demonstrated organizational capacity in raising awareness and addressing reported cyberbullying cases, their ability to detect and prevent underreported instances was limited. Staff’s sense-making efforts regarding cyberbullying were influenced by administrators, yet none of the participants reported strong leadership from principals in implementing provincial and board policies. Consequently, teachers and other staff faced challenges in defining cyberbullying and accessing quality models for addressing this social issue. This study highlights the urgent need for schools to enhance their organizational capacity to effectively address cyberbullying. It emphasizes the importance of proactive measures, standardized approaches, and leadership at the administrative level to foster a culture of prevention and intervention. By gaining a deeper understanding of the limitations in organizational capacity, schools can develop comprehensive strategies to combat cyberbullying and ensure the well-being of students in the digital age.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".