Is It Good to Be Bad? A Longitudinal Analysis of Adolescent Popularity Motivations as a Predictor of Engagement in Relational Aggression and Risk Behaviors
Bibliographic record
Abstract
We examined the impact of adolescents’ popularity motivations on their involvement in relational aggression perpetration and victimization, heavy drinking, and antiauthority behavior, while also considering the role of teens’ perception of their own popularity and psychosocial adjustment. High school students ( N = 986; 50% female; M age = 14.98 years) completed a battery of self-report questionnaires survey in the fall and again, 6 months later. Regression analysis controlling for Time 1 scores confirmed that stronger motivations to achieve or maintain popularity predicted increases in relational aggression perpetration and victimization, and antiauthority behavior. Furthermore, self-reported popularity predicted increases in heavy drinking, but only when popularity motivations were high. Finally, more frequent heavy drinking predicted increases in self-reported popularity over time. Findings emphasize the potential value of addressing adolescents’ popularity motivations in attempts at reducing the aforementioned negative behaviors and associated risks.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".