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Record W1599185801 · doi:10.3968/5393

The Media Use of Liuzhou Folk Song Inheritance in China: According to the Investigation in Yufeng Folk Song Field Under Yufeng Mountain in Liuzhou City

2014· article· en· W1599185801 on OpenAlexvenueno aff
Lei He, Luo Jiang-hua, Jian He

Bibliographic record

VenueHigher education of social science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsInheritance (genetic algorithm)ConnotationModernization theoryFolk cultureCivilizationUrbanizationSociologyAestheticsChinaHistoryAnthropologyPolitical scienceArtLawLinguisticsArchaeology

Abstract

fetched live from OpenAlex

According to the existing research, with modern urban civilization continuously extends into traditional rural society, the survival environment of traditional national culture is suffered from the impact of various aspects, including development of new media technology. However, in the process of the technical urbanization, Liuzhou folk songs have no depression from the impact. On the contrary, because of new methods to be imported into its inheritance, Liuzhou folk songs are presenting a boom. The fieldwork found that Liuzhou folk song inheritance is experiencing shift which is from the traditional mentoring to medium teaching in the process of the modernization. These media include textbooks about folk song creating, improvised dish homemade by singers, the network video resources, and network spontaneous learning community through QQ group and other social software. The active medium use to make folk heritage can adapt to the environment change, and make folk heritage for innovation and development. That has a certain advantage. But in other hand, there has been some limitation that because of the excessive emphasis on written rules and figures of speech, it should have a certain damage on the bearing cultural connotation of Liuzhou folk songs. People should pay more attention to it.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.060
GPT teacher head0.353
Teacher spread0.293 · 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
Published2014
Admission routes1
Has abstractyes

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