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Record W1598413425 · doi:10.1177/0886260515593546

Survival of the Fittest and the Sexiest: Evolutionary Origins of Adolescent Bullying

2015· article· en· W1598413425 on OpenAlexaffabout
Jun-Bin Koh, Jennifer S. Wong

Bibliographic record

VenueJournal of Interpersonal Violence · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychologyMental healthEvolutionary psychologyDevelopmental psychologyAnxietyPoison controlClinical psychologySocial psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

The central idea of evolutionary psychology theory (EPT) is that species evolve to carry or exhibit certain traits/behaviors because these characteristics increase their ability to survive and reproduce. Proponents of EPT propose that bullying emerges from evolutionary development, providing an adaptive edge for gaining better sexual opportunities and physical protection, and promoting mental health. This study examines adolescent bullying behaviors via the lens of EPT. Questionnaires were administered to 135 adolescents, ages 13 to 16, from one secondary school in metro Vancouver, British Columbia. Participants were categorized into one of four groups (bullies, victims, bully/victims, or bystanders) according to their involvement in bullying interactions as measured by the Olweus Bully/Victim Questionnaire. Four dependent variables were examined: depression, self-esteem, social status, and social anxiety. Results indicate that bullies had the most positive scores on mental health measures and held the highest social rank in the school environment, with significant differences limited to comparisons between bullies and bully/victims. These results lend support to the hypothesis that youth bullying is derived from evolutionary development. Implications for approaching anti-bullying strategies in schools and directions for future studies 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 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)0.001
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 score0.043
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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

Citations46
Published2015
Admission routes2
Has abstractyes

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