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Record W4399437576 · doi:10.1002/jad.12359

Using retrospective reports to develop profiles of harmful versus playful teasing experiences

2024· article· en· W4399437576 on OpenAlexaffabout
Naomi C. Z. Andrews, Molly Dawes

Bibliographic record

VenueJournal of Adolescence · 2024
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.433
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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