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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 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".