Differences in Types and Technological Means by Which Mexican High Schools Students Perform Cyberbullying: Its Relationship with Traditional Bullying
Bibliographic record
Abstract
The aim of this study is to determine the differences between types and technological means by which Mexican high school students perform cyberbullying. The effects to perform the role of aggressor and victim in the traditional bullying were also established in the intensity of the reports of cyberbullying. It was used a random cluster sampling with 278 students selected from four high schools, to which they were given two instruments designed expressly for measuring the frequency of types of cyberbullying and the use of technological means for its realization, as well as the frequency of bullying respectively. Results showed that denigration, harassment and exclusion were reported significantly more strongly than the other types of cyberbullying, and that the most frequently used technological medium were social networks. It was also found that performing the role of aggressor (R2=.44) or victim (R2=.37) explained a significant portion of the variance of cyberbullying reports. It was concluded that cyberbullying is a phenomenon that can take various forms and it is related in a complex way with traditional bullying.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".