Bibliographic record
Abstract
In recent years, the term ‘cyberbullying’ has become relatively common in the media, often cited as a contributor to several high-profile suicides of young adolescents. A review of the literature published in 2010 (1) showed that no articles referenced ‘cyberbullying’ before 2004, confirming its recent emergence. There is no universally accepted definition; however, most definitions describe a repeated activity conducted via electronic means with an intent to cause psychological torment. Cyberbullying can take many forms. It can include harassment (insults or threats), spreading rumours, impersonation, outing and trickery (gaining an individual’s trust and then using online media to distribute their secrets) or exclusion (excluding an individual from activities). These activities can be performed via e-mail, instant messaging, text message, social networking sites such as Facebook or Tumblr, and other websites (2). The prevalence of cyberbullying and cyberbullying victimization is difficult to accurately determine. The variable definitions and the typical challenges of accounting for self-reported activities contribute to this difficulty. A study conducted in the United States involving nearly 4000 students in grades 6 to 8 showed that in the preceding two months, 11% of the students had been cyberbully victims, 4% reported acting as cyberbullies, and 7% had been both a cyberbully and a cyberbully victim (3). In a Canadian study published in 2010 involving >2000 students in grades 6, 7, 10 and 11, 25% reported experiencing a cyberbullying event in the previous three months. Eight percent reported acting as a cyberbully, and 25% reported being both a cyberbully and cyberbully victim. The authors postulated that the rates were higher in their study because they did not describe the activity as ‘cyberbullying’, but instead asked about specific behaviours (name calling, threatening, spreading rumours, etc) (4). Cyberbullying differs from traditional bullying in several key ways. Perhaps the most obvious is that it requires some degree of technical expertise – children who are not ‘plugged in’, either through computer, cell phone or video games, do not partake in cyberbullying, either as bullies or victims. Cyberbullying also provides anonymity to the bully not possible with traditional bullying. Because of this, bullies cannot see the reactions of their victims and studies have shown that they feel less remorse (5). Cyberbullying is opportunistic because it causes harm with no physical interaction, little planning and small chance of being caught. Despite this, 40% to 50% of cyberbully victims report knowing who their tormentor is (3). Cyberbullying can be more pervasive than traditional bullying. While traditional bullying is generally limited to school and home is a reprieve, victims of cyberbullying can be reached anywhere, anytime, and the potential audience is huge. This is compounded by the fact that there is a lack of supervision. With traditional bullying, teachers are regarded as enforcers. With cyberbullying, there is no clear authority, and children express reluctance to tell adults for fear of losing computer privileges or being labelled as an informer (6). Studies have also shown that there is a large amount of overlap among traditional bullying and cyberbullying behaviours. Children who act as cyberbullies report high rates of being a traditional bully, and are also traditional and cyberbully victims. Cyberbully victims report high rates of traditional victimization, but are also involved in traditional bullying and cyberbullying activities (3,7). The relationship between traditional bullying and cyberbullying is not well understood, but what is clear about children involved in cyberbullying is that they report high rates of Internet use. Juvonen and Gross (8) found that cyberbully victims were significantly more likely to be heavy Internet users (>3 h/day) than noncyberbully victims (OR 1.45). A study by Mishna et al (4) published in 2012 found that cyberbullies, cyberbully victims and cyberbully/victims were significantly more likely to use the computer for >2 h/day versus students who were not involved in cyberbullying activities.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".