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Record W2046783881 · doi:10.5430/jbar.v4n1p49

The Application of Traditional Chinese Wash Painting in the Modern Fashion Clothing Design

2015· article· en· W2046783881 on OpenAlexvenueno aff
Zhuanye Zhao, Cheng Lu

Bibliographic record

VenueJournal of Business Administration Research · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsClothingPaintingBeautyChinaElement (criminal law)Chinese paintingChinese cultureFashion designCharacter (mathematics)AestheticsBusinessVisual artsAdvertisingArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

With the development of times, great changes have taken place on people’s appreciation of the beauty and demand about garment. The decoration of garment as well as individual character become the trend of times gradually. Chinese traditional wash painting is an important part of Chinese culture, in design of clothing, its special expressive force is a good choice for designer expressing themselves. Therefore, this element is popular among every fashion shows. Because of financial crisis, the road of Chinese export trade is becoming more and more severe, it’s urgent for China to create a road which is full of Chinese culture, the Chinese traditional wash painting is a inspiration origin for native designer. On the base of achievement which is discussed in the past, this paper will pay attention to the similarity between wash painting and garment design as well as the element apply to garment.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.314
GPT teacher head0.374
Teacher spread0.060 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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