Predicting dating behavior from aggression and self‐perceived social status in adolescence
Bibliographic record
Abstract
We investigated the longitudinal associations between self-reported aggression, self-perceived social status, and dating in adolescence using an intrasexual competition theoretical framework. Participants consisted of 536 students in Grade 9 (age 15), recruited from a community sample, who were assessed on a yearly basis until they were in Grade 11 (age 17). Adolescents self-reported their use of direct and indirect aggression, social status, and number of dating partners. A cross-lagged panel model that controlled for within-time covariance and across-time stability while examining cross-lagged pathways was used to analyze the data. The findings revealed that direct aggression did not predict dating behavior and was negatively associated with self-perceived social status in Grade 10. Self-perceived social status in Grade 9 was positively associated with greater use of indirect aggression in Grade 10. Regarding dating, in Grade 9, self-perceived social status positively predicted more dating partners the following year, while in Grade 10, it was higher levels of indirect aggression that predicted greater dating activity the following year. Overall, there were no significant sex differences in the model. The study supports the utility of evolutionary psychological theory in explaining peer aggression, and suggests that although social status can increase dating opportunities, as adolescents mature, indirect aggression becomes the most successful and strategic means of competing intrasexually and gaining mating advantages.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".