Antecedents and outcomes of cyberbullying among Chinese university students: verification of a behavioral pathway model
Bibliographic record
Abstract
Introduction Cyberbullying is a commonly-seen and hotly-debated social topic around the globe. This negative behavior is the source of many disastrous events, and so leading government bodies, organizations, schools and social communities attach great importance to addressing this topic. However, there is still much work to do in order to be clear about the causes of cyberbullying. Methods The previous research cases were mostly viewed from the victims’ perspectives; however, there is no comprehensive understanding of the perpetrators’ viewpoints. Therefore, based on Social Cognitive Theory (SCT) and analysis of discussion in the literature, the following six variables were chosen as the focus of this study: overconfidence, excessive moral sense, cyberbullying, perceived value, happiness, and continued cyberbullying intention. This study established a research model of continued cyberbullying intention, which was verified by Structural Equation Modeling. In order to achieve the aims of the study, Chinese university students with an average age of 20.29 (SD = 1.43) were recruited as participants, from whom 1,048 valid questionnaires were collected. Results The research results are as follows: 1. Overconfidence and excessive moral sense positively predicted cyberbullying behaviors; 2. Overconfidence positively predicted excessive moral sense; 3. Cyberbullying positively predicted perceived value and sense of happiness; and 4. Perceived value and sense of happiness positively predicted continued cyberbullying intentions. Conclusion Students’ biased self-perception significantly predicts their cyberbullying behaviors and continued cyberbullying intention. What is more, it is interesting to learn that perpetrators will continue to exhibit cyberbullying behaviors when they believe that what they do (cyberbullying) is valuable or allows them to experience positive feelings; this requires our attention.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".