Is teasing meant to be mean or nice? Retrospective reports of adolescent social experiences and teasing attitudes
Bibliographic record
Abstract
Abstract Peer teasing has contradictory conceptualizations, including teasing that is playful, and teasing that is akin to bullying. In addition to individuals potentially having different conceptualizations of teasing, and despite the inherently social nature of teasing, little is known about the social correlates of teasing attitudes. The current study aimed to examine multifaceted teasing attitudes (i.e., aggressive, affectionate, or romantic interest teasing), and to assess how past social experiences (victimization, popularity, social satisfaction/self‐concept) relate to teasing attitudes. Young adults ( N = 437, 17–25 years old, 65% female) reported on multifaceted teasing attitudes on a Teasing Attitudes Scale developed for this study. Participants also reported retrospectively on adolescent social experiences. Findings validated the Teasing Attitudes Scale, showing that young adults have distinct attitudes toward teasing as aggressive, affectionate, and indicating romantic interest. Participants who reported victimization by bullying, lower popularity (girls only), social satisfaction, and social self‐concept were more likely to view teasing as aggressive. Participants who had been victims of non‐bullying aggression viewed teasing as affectionate, and those with high social satisfaction and self‐concept viewed teasing as for romantic interest purposes. Implications for understanding the complexities of teasing and its associations with individual attitudes and adolescent social experiences will be discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".