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Record W4224257485 · doi:10.1177/1321103x221081984

Why do singers use imagery?

2022· article· en· W4224257485 on OpenAlexaff
Brianna DeSantis, Sarah Deck, Craig Hall, Sophie Louise Roland

Bibliographic record

VenueResearch Studies in Music Education · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsSingingPsychologyAthletesAnxietyMental imageFeelingApplied psychologySocial psychologyCognition

Abstract

fetched live from OpenAlex

Previous sport research has demonstrated that athletes of higher levels employ imagery more than low-level athletes. Because there is currently little research on imagery’s application in singers, the purpose of the present study was to investigate whether this finding is reflected in low-level and high-level singers. A study-specific questionnaire was developed that examined singers’ imagery use. The questionnaire consisted of four subscales that assessed vocal technique, performance anxiety regulation, goals, and characterization. It was found that singers used imagery most for characterization (i.e., portraying a character or feeling), followed by goals, vocal technique, and performance anxiety regulation. No differences existed between professional and student singers’ imagery use. There was a significant difference between males and females on the characterization subscale, suggesting that female singers may use imagery for characterization more so than males. Introducing this approach to imagery to singers and teachers of singing has the potential to influence music education in school settings and impact curriculum development.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.388
GPT teacher head0.559
Teacher spread0.171 · 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.

Study designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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