Predicting Psychosocial Maladjustment in Emerging Adulthood From High School Experiences of Peer Victimization
Bibliographic record
Abstract
= 2.54) completed online measures assessing retrospective accounts of their experiences of different forms of peer victimization during high school (i.e., sexual, physical, verbal, social, and cyber) and their current psychosocial adjustment (i.e., self-esteem, depressed affect, and loneliness). Three separate hierarchical multiple regressions were conducted to determine whether different indices of negative psychosocial adjustment are more strongly predicted by experiencing sexual or nonsexual forms of peer victimization. Although many university students recalled experiencing sexual peer victimization in high school at least once at an even higher percentage than verbal and social forms of peer victimization, the results of the present study suggest that social peer victimization in high school predicts higher levels of depressed affect and loneliness in university students than sexual peer victimization experienced in high school. Surprisingly, the young adults reporting higher levels of cyber peer victimization in high school were less lonely in university. Although the hypothesized relationships between each form of peer victimization and specific indices of psychosocial functioning were not consistently supported, these findings suggest that the form of peer victimization matters and may be differentially associated with well-being in emerging adulthood. It is important that future research explores how individual characteristics may further predict varied experiences of peer victimization and the long-term impact of those experiences.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".