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Status and Gender Differences in Early Adolescents' Descriptions of Popularity

2008· article· en· W2047404495 on OpenAlexaff
Leanna M. Closson

Bibliographic record

VenueSocial Development · 2008
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPopularityPsychologyDevelopmental psychologyClothingPeer acceptanceSocial psychologyPeer group

Abstract

fetched live from OpenAlex

Abstract This study examined gender and status differences among sixth through eighth grade early adolescents' (N = 387) descriptions of what it means to be popular. More boys than girls specified being cool, athletic, funny, and defiant/risky, whereas more girls than boys identified wearing nice clothing, being attractive, mean, snobby, rude, and sociable as descriptors of popularity. Descriptions also varied as a function of the individual's status: adolescents who were perceived as popular described popularity primarily in positive terms, whereas adolescents perceived as average and unpopular used both positive and negative terms. Compared with their same‐gender peers, more popular boys indicated being cool, attractive, and athletic, whereas more popular girls specified being athletic and liked. Compared with popular girls, more average girls used the terms mean and conceited in their descriptions, whereas more average and unpopular girls indicated the term snobby. This study illustrates the complexity and variability in early adolescents' social constructions of popularity.

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)0.004
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.304
Teacher spread0.227 · 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

Citations156
Published2008
Admission routes1
Has abstractyes

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