Cyber Bullying in Chinese Web Forums: An Examination of Nature and Extent
Bibliographic record
Abstract
IntroductionAdolescent school violence is a common and significant problem in many countries across the globe (Arseneault, Walsh, Trzesniewski, Newcombe, Caspi, & Moffitt, 2006; Beran & Li, 2005, 2007; Erdur-Baker, 2010; Frost, 1991; Hazler, Hoover, & Oliver, 1992; Ma, 2001; Olweus, 1993; Sharp, Thompson, & Arora, 2000; Wang, Iannotti, & Nansel, 2009). Often, violence among youths involves some component of bullying, wherein individuals repeatedly experience some negative action by another young person who attempts to disrupt, injure, or otherwise cause discomfort for their victim (Olweus, 1993). The impact of bullying can be quite severe, often causing depression and health concerns for victims (Kaltiala-Heino, Rimpela, Marttunen, Rimpela, & Rantanen, 1999; Klomek et al., 2008; Kumpulainen & Rasanen, 2000; Nansel, Overpeck, Haynie, Ruan, & Scheidt, 2003; Nansel et al., 2001; van der Wal, de Wit, & Hirasing, 2003), and attempted suicide (Klomek, Marrocco, Kleinman, Schonfeld, & Gould, 2007; Klomek et al., 2009). In fact, some researchers argue that bullying is a major public health concern requiring significant research and resources (Nansel, Overpeck, Pilla, Ruan, Simons- Morton, & Scheidt, 2001).As the Internet and computer-mediated communications technologies are increasingly inexpensive and available, the opportunities for individuals to engage in bullying via electronic methods, or cyber bullying, has increased significantly (Beran & Li, 2005, 2007; Finkelhor, Mitchell, & Wolack 2000; Hinduja & Patchin, 2008, 2009; Snider & Borel, 2004; Wolack, Mitchell, & Finkelhor, 2006; Ybarra 2004). Research on cyber bullying has primarily focused on student populations in the United States and Canada, due to several high profile incidents where cyber bullying was related to incidents of suicide among youth (Beran & Li, 2005, 2007; Finkelhor et al., 2000; Hinduja & Patchin, 2008, 2009; Li, 2006; Marcum, 2008; Wolack et al., 2006; Ybarra, 2004). These studies indicate that there is significant emotional and mental health concerns generated by cyber bullying experiences (e.g. Hinduja & Patchin 2008; van der Wal et al., 2003; Ybarra 2004). Few researchers have, however, actively examined the content of bullying messages to consider the frequency of multiple forms of bullying, and the tenor of the messages posted by bullies to understand how messages are developed and targeted (Hinduja & Patchin, 2008, 2009). As a consequence, it is unclear how the process and experience of bullying occurs.Considering the significant challenges posed by cyber bullying, researchers across the globe are beginning to examine this phenomenon (Erdur-Baker, 2010; Erdur-Baker & Kavsut, 2007; Li, 2008; McLoughlin, Meyricke, & Burgess, 2009; Wolack et al., 2006). Studies utilizing US populations suggest that cyber bullying is a common problem among juvenile populations, though prevalence rates vary depending on the sample and definition of bullying used (Marcum, 2010; Hinduja & Patchin, 2008; Wolack et al., 2006). Similar research has found cyber bullying to be a growing problem in Australia (McLoughlin et al., 2009), Canada (Beran & Li, 2005, 2007), and Turkey (Erdur-Baker, 2010; Erdur- Baker & Kavsut, 2007). Few researchers have, however, examined the issue of cyber bullying in developing nations, particularly Asia (Huang & Chou, 2010; Li, 2008). In fact, China is the most populace nation in the developing world, and has experienced an explosion in Internet use over the last decade (Central Intelligence Agency, 2010). In fact, one of the only studies examining cyber bullying in China found that 33 percent of children had experienced cyber bullying, while a very small percentage actually engaged in cyber bullying themselves (Li, 2008). Chinese students appear more likely to report victimization experiences to teachers or adults suggesting that adults are more likely to intervene on behalf of a victim (Li, 2008). …
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".