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Record W6994302365

Entrainment

2019· dissertation· en· W6994302365 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersMcGill University
KeywordsRhythmEntrainment (biomusicology)Movement (music)Perception
DOInot available

Abstract

fetched live from OpenAlex

Entrainment is a composition for six percussionists.The piece is in seven movements which explore different aspects of rhythmic and metric perception and cognition.In order to express the different rhythmic techniques used in the Entrainment, I develop a theoretical apparatus that allows the analysis of concurrent temporal streams.The first two movements deal with how the brain reconstrues rhythmic material into different meters based on context.The third and fourth movements explore how the brain understands meter when given very little rhythmic information.The fifth and sixth movements explore how the brain understands meters which are out of phase with one another.The final movement serves as a culmination of the various rhythmic techniques used throughout the piece.In addition to the rhythmic elements, the movements contribute to numerous large-scale trajectories pertaining to tempo, timbre, and dynamics, leading to the final movement. RésuméEntrainment est une œuvre pour six percussionnistes.L'œuvre en sept mouvements explore plusieurs éléments de la perception et de la cognition du rythme et du mètre.Afin d'exprimer les différentes techniques rythmiques utilisées dans Entrainment, j'ai développé un modèle théorique qui permet l'analyse des strates temporelles simultanées.Les deux premiers mouvements examinent comment le cerveau réinterprète un matériel rythmique donné dans une nouvelle organisation métrique en s'appuyant sur le contexte.Les troisième et quatrième mouvements étudient la façon dont le cerveau comprend le mètre quand il y a peu d'information rythmique.Les cinquième et sixième mouvements examinent comment le cerveau comprend le décalage métrique entre les différentes strates temporelles.Le dernier mouvement utilise toutes les techniques employées dans les mouvements précédents.En dehors des éléments rythmiques, le timbre, les nuances et le tempo forment des trajectoires à grande échelle qui mènent au dernier mouvement.1 Martin Clayton, "What is Entrainment?Definition and applications in musical research."Empirical Musicology Review 7, no.1-2 (2012): 49. 2 See Justin London.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.067
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0670.023

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 head0.030
GPT teacher head0.268
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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