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Record W1770027727 · doi:10.3968/4655

The Attributes, Functions and Divisions of Folk Arts Market

2014· article· en· W1770027727 on OpenAlexvenueno aff
Zhang Zhong-bo

Bibliographic record

VenueHigher education of social science · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsHandicraftExhibitionIndustrialisationContext (archaeology)Object (grammar)TourismArts in educationGlobalizationPerforming arts educationVisual artsSociologyPolitical scienceArtHistoryComputer scienceLaw

Abstract

fetched live from OpenAlex

Folk Arts industrialization has three systematical factors, which are the subject, object and medium of folk arts. The coordination of these three factors can bring the sustainable and orderly development of folk arts. The resource of folk arts is the object of folk arts industrialization; the production operators and the consumer consist of the subject, and folk arts market as the medium of folk arts industrialization which connects the subject and object, is the bridge that links the the production of folk arts and the consuming of folk arts, and promotes the production of folk arts and guide the consuming of folk arts. Under the arts industry context, based on the attributes of the resource of folk arts and the condition of the demands of market, strengthening market development consciousness, putting forth effort on tourism, performing arts market, handicrafts market, exhibition market, movie and television market and other divisional markets are the preconditions to make the process of folk arts industrialization to carry on orderly.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0060.006
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.279
Teacher spread0.254 · 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 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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