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Record W2916838169

THE REALIA OF IMMITATIVE DANCES IN FOLKLORE OF INDIGENOUS PEOPLES: THE YUKAGIRS (THE REPUBLIC OF SAKHA YAKUTIA) AND THE INUIT (CANADA)

2015· article· ru· W2916838169 on OpenAlexaboutno aff
K I Zakharova

Bibliographic record

VenueМеждународный научно-исследовательский журнал · 2015
Typearticle
Languageru
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFolkloreIndigenousRealiaEthnologyGeographyHistoryArchaeologyLinguisticsEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Статья рассматривает сходства и различия в фольклоре малочисленных народов Республики (Саха) Якутия и инуитов Канады. Подражательные танцы – один из элементов фольклора, который требует детального исследования. В данной статье представлен сравнительный анализ различных видов танцев юкагиров и инуитов. Рассмотрев детальный анализ подражательных танцев у инуитов и юкагиров можно сделать вывод о том, что фольклор двух народностей имеет схожести в подаче и элементах их содержания.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.019
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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
Published2015
Admission routes1
Has abstractyes

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