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Record W2049535209 · doi:10.1177/0305735606068106

The effects of various physical characteristics of high-level performers on adjudicators’ performance ratings

2006· article· en· W2049535209 on OpenAlexaff
Charlene Ryan, Joel Wapnick, Nathalie Lacaille, Alice-Ann Darrow

Bibliographic record

VenuePsychology of Music · 2006
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyAttractivenessPerceptionPhysical attractivenessSocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the relationship between physical attractiveness and performance ratings of high-level pianists. Eighteen of the 30 competitors who participated in the Eleventh Van Cliburn International Piano Competition were rated by undergraduate and graduate music students and faculty. Participants were divided into three groups who rated the performances under either audio, audiovisual, or visual-only conditions. Visual-only participants rated performers on physical attractiveness, dress, and stage behaviour. Results suggest that high-level pianists are not affected in the same way by the apparent attractiveness bias that has been found in studies of novice and college-level musicians. Performers rated low on behaviour received consistently higher performance scores than high behaviour performers regardless of treatment condition (audio or audiovisual). Performers who rated high on each of the visual components benefited from the audiovisual condition, as compared with the audio-only condition, on note accuracy, but not on five other measures of performance. Interactions between each visual component (attractiveness, dress, and behaviour) and gender of the rater raise questions about gender differences in the perception of attractiveness.

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 score0.527
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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

Citations36
Published2006
Admission routes1
Has abstractyes

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