Using retrospective reports to develop profiles of harmful versus playful teasing experiences
Bibliographic record
Abstract
INTRODUCTION: The current investigation's central goal was to elucidate the complex features of peer teasing episodes that individuals use to interpret teasing as harmful versus playful. METHOD: In 2022-2023, we used semistructured interviews to gather retrospective reports of K-12 peer teasing experiences from a sample of 27 students from a university in southern Ontario, Canada (18-25 years old, 63% female, 78% White). RESULTS: Content analysis revealed the multifaceted nature of teasing, with participants defining teasing as harmful, playful, or including elements of both harm and pleasure. Harmful teasing experiences often included content that was sensitive to the target, occurred between both friends and nonfriends, and often included a power differential with the teasing perpetrator having more power than the target. Targets recalled negative emotional responses, with behavioral responses to mitigate the situation and reduce further teasing. In contrast, playful teasing often occurred between friends or close friends, was often motivated by positive interpersonal motives (e.g., for encouragement), and had positive impacts on the relationship between perpetrator and target. However, despite benign intent, some playful teasing was marked by negative emotional responses and feelings of harm. CONCLUSIONS: Results have implications for uncovering the nuanced and complex nature of teasing, and provide a preliminary profile of harmful versus playful teasing interactions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".