The role of narcissism and self-esteem in predicting peer-oriented and dating aggression in a sample of high-risk youths
Bibliographic record
Abstract
In the psychological literature, low self-esteem has frequently been linked to aggressive behaviour in both youth and adults. These findings, however, have been challenged and it has been proposed that narcissism is actually the personality characteristic that gives rise to aggression towards others. Research investigating the relationship between narcissism, self-esteem and aggression in adolescents has emphasized the importance of examining both personality constructs to gain a better understanding of aggressive behaviour. The primary focus of this study is to expand on the literature examining narcissism, self-esteem and aggression in adolescents by investigating the relationship between these constructs in a sample of high-risk youth. Furthermore, this research will not only investigate peer-oriented aggression, but will extend the hypotheses to incorporate attitudes about dating aggression. Participants included 110 male and female youth between the ages of 12 to 18 years. Results indicated that narcissism predicts both peer-oriented aggression as well as attitudes towards aggression in a dating relationship for both male and female youth. Self-esteem was found to significantly predict peer-oriented aggression and attitudes towards dating aggression, but only when examined in conjunction with narcissism. No significant gender effects were found. Discussion focuses on the overlap between narcissism and self-esteem in predicting aggression, as well as an examination of different developmental trajectories which lead to aggressive behaviour.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".