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Record W2786733404 · doi:10.5539/ach.v10n1p76

Myth in the History and Modern World of She People: The Preservation, Transmission and Uses of Epic Stories in the Ethnic Group of South-Eastern China

2018· article· en· W2786733404 on OpenAlexvenueno aff
Sai Yan

Bibliographic record

VenueAsian Culture and History · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyEthnic groupModernityEPICContext (archaeology)ChinaSociologyAestheticsHistoryEthnographyCultural transmission in animalsLiteratureGender studiesAnthropologyArtPhilosophyClassicsEpistemologyArchaeology

Abstract

fetched live from OpenAlex

This article gives an ethnographic account of the epic tradition of She people in China, noting the dual transmission methods of stories, in which the core of the story is preserved in pictorial depictions within primarily sacred contexts, whilst more flexible oral retellings fit the stories to the daily life of the She in modern China. The article contrasts western views of myth as a feature of a pre-modern way of life which has lost its relevance in the modern world with common understandings of myth in China as factually based and relevant to contemporary life. The article shows how the She deploy their mythic heritage to reinforce their ethnic identity and to pursue material interests within the context of modernizing Chinese state, numerically and politically dominated by the Han majority, and draws upon the work of Claude Levi-Strauss to suggest that myth remains an important component of Chinese modernity.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.017
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
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 head0.040
GPT teacher head0.269
Teacher spread0.230 · 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
Published2018
Admission routes1
Has abstractyes

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