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Record W2977849157 · doi:10.15181/rh.v25i0.1978

ETHNOGRAPHIC REGIONS IN THE TERRITORY OF CONTEMPORARY CHERKASY DISTRICT BY FEATURES OF RITUAL FOLK SONGS

2019· article· en· W2977849157 on OpenAlexaff

Bibliographic record

VenueRes Humanitariae · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsSingingFolklorePolyphonyMelodyFolk songEthnographyEthnomusicologyStyle (visual arts)TypologyFolk musicHistoryMusicalGeographyLiteratureArtAnthropologySociologyArchaeologyAcoustics

Abstract

fetched live from OpenAlex

We have to define at least three different regional folk singing traditions in the central part of Dnipro river localities in Ukraine. They are located in the territory of contemporary Cherkasy district. We have Podillia region with much more archaic folk songs genres and enough clear their musical stylistic features. We also have Naddniprianshchyna and Poltavshchyna regions with colourful and various way enriched polyphonic features of regional folk singing traditions. The main goal of our ethnomusicological activities we see here in recording continuity of such singing folklore traditions from all Cherkasy district followed by their scientific research studies in their style, genre, rhythmic and melodic typology aspects, as well as mapping of particular macro- and micro-zones in various mentioned above parameters.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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