Fighting Cyberbullying in Schools: What Law Enforcement, Schools, and Parents Can Do
Bibliographic record
Abstract
Schools throughout the United States are facing an epidemic. While some parents might look at bullying as a part of growing up, in fact it has reached epidemic proportions across the country. More than 3.2 million students are victims of bullying each year, according to DoSomething.org, a social issues advocacy group. Every day, nearly 160,000 children miss school because they are scared of being bullied, notes the National Education Association (NEA). Bullying through the use of electronic devices (smartphones, laptops, etc.) corresponds with the dramatic rise in the use of mobile technology among young people. The cyberbullying epidemic among students in the United States, Canada, the United Kingdom and other countries where students have easy access to the Internet has resulted in a number of high profile and alarming national headlines. Many of these cyberbullying incidents have seen tragic ends, including student suicides and deadly school shootings. In this white paper, Thomson Reuters draws upon the expertise of several experts to help law enforcement, school resource officers and security officials better understand cyberbullying, related student-on-student school violence, and the measures that these professionals may employ to reduce the risk and investigate cyberbullying and school violence incidents.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".