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Record W2811382052 · doi:10.18061/emr.v12i3-4.6389

Tapping to Carter: A Response to Fischinger and Dyck-Hemming

2018· article· en· W2811382052 on OpenAlexaff
Poudrier

Bibliographic record

VenueEmpirical Musicology Review · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTappingInterpretation (philosophy)DeterminacyMusicalPianoFinger tappingStyle (visual arts)Cognitive psychologyPsychologyCognitive scienceBeat (acoustics)LinguisticsComputer scienceCommunicationHistoryPhilosophyMathematicsVisual artsArt

Abstract

fetched live from OpenAlex

This short essay responds to issues raised by Fischinger and Dyck-Hemming in their commentary on this author's article "Tapping to Carter: Mensural Determinacy in Complex Rhythmic Sequences." Borrowing Christopher Hasty's concept of mensural determinacy, I used an excerpt of Elliott Carter's 90+ for piano (1994) as source materials for a tapping experiment aimed at: (1) testing the hypothesis that style-specific expertise correlates with lower tapping variability; (2) exploring the influence of an implied beat on participants' interpretation of the underlying pulse, as shown by spontaneous tapping; and (3) exploring the influence of a subset of musical parameters as well as participants' characteristics on tapping behavior. This response aims to clarify the methodology employed, especially with reference to the interpretation of the results; it also addresses concerns raised by the reviewers in relation to the use of the tapping paradigm to investigate Carter's compositional language. While the experimental method necessarily limited the interpretation of participants' ongoing experience of pulse, the findings provide useful insights on the role of style-specific expertise and call for a more diverse and disciplinarily unbounded methodological approach to the study of musical communication.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.118
GPT teacher head0.403
Teacher spread0.285 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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