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Record W154980203 · doi:10.5206/notabene.v6i1.6588

Performance Practice of Early American Hymnody: Tempo and the “Moods of Time”

2013· article· en· W154980203 on OpenAlexvenueno aff
Erin Fulton

Bibliographic record

VenueNota bene · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRepertoireNotationHistoryLiteratureLinguisticsArtPhilosophy

Abstract

fetched live from OpenAlex

Modern time signatures indicate metrical organization in notated music. However, in most American hymnals and psalters published between 1721 and 1809, time signatures also signify very specific tempi. This notational practice, further removed from modern usage than any other element of this music, derives from proportional notations abandoned in art music in the seventeenth century. As technically complex music was published using this notation in the 1760s, these time signatures began to be used more subtly. In combination, they provide metrical effects unlike those possible with modern time signatures: doubling or halving tempo, or maintaining the pulse while altering its division or larger metric organization. Viewed from the perspective of modern notation, these functions diverge from their appearance. This article clarifies the correlation between time signature and tempo indicated in eighteenth- and early nineteenth-century American tunebooks (hymnals), arguing for its inclusion in modern performances of this repertoire. Internal evidence and related pedagogical practices suggest these tempi were intended to be observed; most early American theorists, composers, and compilers advocated adherence. Any revival of repertoire first published in this notation, including the works of such composers as William Billings, Daniel Read, and Supply Belcher, would profit by observing these tempi. In a repertoire frequently devoid of interpretive markings, time signatures provide invaluable clues to performers.

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 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: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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
Published2013
Admission routes1
Has abstractyes

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