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Record W2948205621 · doi:10.33906/musicologist.439343

Shift and Transformation in Salento: Investigating Change in the Polyphonic Structure and Performance Practice of Canti Polivocali in Southern Puglia

2018· article· en· W2948205621 on OpenAlexaff
Mario Morello

Bibliographic record

VenueMusicologist · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsYork University
Fundersnot available
KeywordsPolyphonyMusicalField (mathematics)SingingGeographyRepertoireHistoryLinguisticsVisual artsArtLiteratureAcoustics

Abstract

fetched live from OpenAlex

This paper will focus on documenting change and transformation of polyphonic singing in the Salento region of lower Puglia (southern Italy), based on a comparative analysis of early field recordings and recently collected materials. Cantipolivocali (“multi-voiced songs”), the repertoire of vocal polyphony distinct to the Salento region, was once widely practiced throughout the region with subregional musical dialects and a rich repertory of local variants. This rich musical activity was captured in early field recordings by the first major wave of researchers in the field, notably between the mid-1950s to late 1960s by ethnomusicologists Diego Carpitella, Alan Lomax, Gianni Bosio, and Clara Longhini. After mass emigration and significant changes in traditional music’s positioning in contemporary Italian culture, the tradition of this singing in Salento has shifted considerably in both its polyphonic structure and how it is situated in cultural spaces. By comparing recordings of the aforementioned early collections with field recordings recently collected by the author through analysis, this paper will aim to document these changes and identify the significant transformations of cantipolivocali in Salento.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.992

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)0.0000.000

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 head0.107
GPT teacher head0.268
Teacher spread0.162 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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