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Record W7065093104

Cyclical Structures in Central Javanese Skeletal Melodies

2019· other· en· W7065093104 on OpenAlexaff

Bibliographic record

VenueYork University Digital Library (York University) · 2019
Typeother
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsYork University
Fundersnot available
KeywordsMelodyTimelineChord (peer-to-peer)Feature (linguistics)RhythmMusical
DOInot available

Abstract

fetched live from OpenAlex

Cyclical patterning of rhythm is a prominent feature in many musical traditions.To name a few: in early European music, isorhythmic motets; in later European music, ground basses, bassi ostinati, and themes and variations; in vernacular European music, rounds, catches, and strophic songs; in American-derived music, chord progressions and changes in jazz and blues; in sub-Saharan idioms and their diasporic extensions, percussive timelines and claves; in Middle Eastern and South Asian music, awzān, īqā'āt, uşūls, and tālas.The present report considers cyclical patterning in the longest, most complex pieces of traditional music for instrumental ensembles in Central Java, namely, gendhings.In particular, this report focuses on the initial, longest sections of gendhings, termed mérongs.Whereas the general analytical approach outlined here is, in principle, applicable to any of the genres just mentioned, its specific application is directly relevant to Central Javanese practice.In this regard, the approach outlined here differs from European-derived analyses of time intervals and melodic form.Rather than abstract concepts of meter or hypermeter, the point of departure is continually recurring cycles of immediately audible concrete time-intervals and analytical results are interpreted in terms of behavioral psychology.Instead of describing melodic form in terms of discrete segments conveyed by, for instance, the letters A, B, C, etc., the present report identifies both overlap and gaps between segments and connects them directly to particular beats and time spans within rhythmic cycles.Also unusual is the present study's analysis of aggregated works rather than individual pieces.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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