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Record W4308999691 · doi:10.1177/02557614221136280

Perceptions of improvements in piano performance following a Body Mapping workshop

2022· article· en· W4308999691 on OpenAlexaff
Teri Slade, Gilles Comeau, Donald Russell

Bibliographic record

VenueInternational Journal of Music Education · 2022
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsCarleton UniversityUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsPianoPsychologyPerceptionMovement (music)MusicalCLIPSTest (biology)Quality (philosophy)Cognitive psychologyMultimediaComputer scienceVisual artsArtificial intelligence

Abstract

fetched live from OpenAlex

It is becoming increasingly popular for musicians to study Body Mapping, a method of body movement education, to improve both body movement and musical quality. In Body Mapping workshops, observers frequently claim that they can both see and hear improvements in the performance, yet previous research does not support this anecdotal evidence. In the present study, pianists received a full day Body Mapping workshop and a panel of judges, blind to condition, evaluated audio and silent video clips of performances recorded the day before and the day after the workshop. In Experiment 1, judges were able to identify the post-test recordings by silent video at a rate significantly better than chance, but not with audio alone. In Experiment 2, ratings of quality of body movement were significantly higher for post-test silent video recordings, but no such effect was observed with audio alone. The present findings suggest that there are visible but not audible changes to the pianists’ performance. We discuss visual dominance as a possible explanation for these findings.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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