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Record W2120180362 · doi:10.1017/s0954579402001050

Peer to peer sexual harassment in early adolescence: A developmental perspective

2002· article· en· W2120180362 on OpenAlexaffabout
Loren E. McMaster, Jennifer Connolly, Debra Pepler, Wendy Craig

Bibliographic record

VenueDevelopment and Psychopathology · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsQueen's UniversityYork University
Fundersnot available
KeywordsHarassmentPsychologyPsychosocialPeer groupDevelopmental psychologyPerspective (graphical)Peer reviewClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The goal of this study was to examine sexual harassment in early adolescence. Available data indicate that peer to peer sexual harassment is prevalent in high school and is associated with psychosocial problems for both victims and perpetrators. For the present study, we adopted a developmental contextual model to examine the possibility that this behavior develops during the late elementary and middle school years and is linked to the biological and social changes that occur at this time. Youths from Grades 6-8 (N = 1,213) enrolled in seven elementary and middle schools in a large south-central Canadian city were asked to report on their sexual harassment behaviors with same- and cross-gender peers; their pubertal development, and the gender composition of their peer network. The results revealed that cross-gender harassment was distinct from same-gender harassment, increased in frequency from Grade 6 to Grade 8, and was linked to pubertal maturation and participation in mixed-gender peer groups. The implications of a developmental contextual model for understanding the emergence of this problematic behavior in adolescence are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.346
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations297
Published2002
Admission routes2
Has abstractyes

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