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Record W2142165516 · doi:10.1177/0305735602302003

Perceptions of Musicians: Gender Stereotypes and Social Role Theory

2002· article· en· W2142165516 on OpenAlexaff
Kenneth M. Cramer, Erin Million, Lynn A. Perreault

Bibliographic record

VenuePsychology of Music · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyFlutePerceptionSocial psychologyHappinessMusicalSemantic differentialScale (ratio)MasculinityDevelopmental psychology

Abstract

fetched live from OpenAlex

Research reports of consistent gender differences in the selection of musical instruments (i.e. trumpets for boys and flutes for girls). As observed in other domains, social perception research shows comparably negative evaluations of individuals engaged in activities atypical to their gender. The present study investigated college students' evaluations of fictitious male or female musicians playing either a masculine (drum and tuba) or feminine (flute or harp) instrument. Using a semantic differential scale, the 98 students were asked their impressions of each of the four fictitious musicians according to masculine (dominant, leadership, activity), feminine (warm, sensitive, caring), or gender-neutral (adjustment, happiness, success) descriptors. Results showed that female musicians were perceived as more dominant, active, and better leaders than male musicians. Moreover, musicians of feminine instruments were perceived as more caring, warm, sensitive, and better adjusted; but less dominant and prone to leadership than musicians of masculine instruments. Finally, the hypothesised interaction between instrument gender and musician sex was significant. Specifically, males and females were judged equivalently for masculine instruments, but males playing feminine instruments were perceived as less dominant, active, and better leaders than females playing the same instruments. Implications for gender stereotypes and social role theory are discussed, and directions for future research are outlined.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.332
Teacher spread0.259 · 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 designQualitative
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

Citations53
Published2002
Admission routes1
Has abstractyes

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