Bibliographic record
Abstract
Bullying and cyber bullying are prevalent across the globe, and they have severe ramifications for both people and communities. Despite the fact that the quantity of research papers on the subject has grown dramatically throughout the course of history, many concerns about the phenomenon remain unresolved today. In spite of the fact that technology offers many advantages to young people, it also has a dark side,’ in that it may be exploited to do damage not just by certain adults, but also by young people themselves. Email, texting, chat rooms, mobile phones, mobile phone cameras, and online sites may all be used by young people to harass their classmates, and in fact, they often are. It has now become a worldwide issue, with many instances recorded in the United States, Canada, Japan, Scandinavia, and the United Kingdom, as well as in Australia and New Zealand, among other countries. Although it is becoming more prevalent, this issue has not yet gotten the attention it deserves and is practically missing from the study literature. This article examines definitional problems, the prevalence and potential effects of cyber bullying, as well as various preventive and intervention methods, all of which are discussed in detail.
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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".