Evolutionary perspective on indirect victimization in adolescence: the role of attractiveness, dating and sexual behavior
Bibliographic record
Abstract
We studied indirect victimization from an evolutionary perspective by examining links between this type of victimization and several indicators of attractiveness (past sexual behavior, dating frequency and physical appearance). Two thousand three hundred and nineteen (56% female) students (ages 13-18) from a region of southern Ontario, Canada, completed self-report measures of indirect victimization, physical appearance, dating frequency, recent sexual behavior (number of partners in previous month) and past sexual behavior (number of lifetime partners minus number of partners in previous month) as well as indexes of depression, aggression and attachment security, which were used to control for psychosocial maladjustment. Consistent with an evolutionary framework, physical appearance interacted significantly with gender, wherein attractive females were at greater risk for indirect victimization, whereas for males physical attractiveness was a protective factor, reducing risk of victimization. Physical appearance also interacted with grade, being inversely related to indirect victimization for younger adolescents and having a nonsignificant association with victimization for older youth. Finally, recent sexual behavior was associated with increased risk of indirect victimization for older adolescents only, which we discussed with regard to peer perceptions of promiscuity and short-term mating strategies. These findings have important implications for the development of interventions designed to reduce peer victimization, in that victims of indirect aggression may represent a rather broad, heterogeneous group, including attractive individuals with no obvious signs of maladjustment.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".