Cyberbullying, cyber aggression, and cyber victimization in relation to adolescents’ dating and sexual behavior: An evolutionary perspective
Bibliographic record
Abstract
This study examined adolescents' cyberbullying, cyber aggression, and cyber victimization from an evolutionary perspective, extending previous research showing that traditional forms of bullying, aggression, and victimization are associated with reproductively relevant outcomes. Consistent with hypotheses based on theory and research linking bullying and aggression to intrasexual competition for mates, results indicated that cyber victimization was positively associated with a number of dating and sexual partners. Findings for cyber aggression were more complex, depending on the degree of cyber victimization experienced by the perpetrator, and the balance of power between the perpetrator and victim. Specifically, nonbullying cyber aggression by perpetrators with equal or less power than the victim had stronger positive relations with the number of dating or sexual partners when perpetrators experienced a high level of cyber victimhood. In contrast, cyberbullying by perpetrators with more power than the victim was negatively associated with the number of dating partners when the perpetrators' exposure to cyber victimization was low. Although cyber aggression and cyber victimization are new forms of aggression that involve the use of modern electronic devices, the results of this study demonstrate the usefulness of viewing this behavior from an evolutionary perspective and show that adolescents are likely to use cyber aggression against rivals in the context of intrasexual competition for mates.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".