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Record W3004017175 · doi:10.5539/ass.v16n2p45

Comparison Between Atlas in Xinjiang of China and Patola in Gujarat of India

2020· article· en· W3004017175 on OpenAlexvenueno aff
Weizhu An, Sudha Dhingra, Feng Zhao

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsWeavingAtlas (anatomy)ChinaDyeingTyingColoredGeographyVisual artsArtEngineeringComputer scienceArchaeologySociologyAnthropologyBiologyMechanical engineering

Abstract

fetched live from OpenAlex

Ikat is an ancient technique by which colored patterns are formed by tying and dyeing threads before they are woven. Ikat is an ancient resist dyeing technique in which the yarns are tied and dyed according to a specific pattern prior to weaving. This paper focuses on Atlas and Patola, the most famous ikat fabrics in China and India respectively as the research objects. The effort is to provide comparative analysis from the aspects of weaving technology, pattern style, composition characteristics and colors, based on literature and images. Although they are similar in production process, they have very different characteristics due to cultural background, religion, environment and aesthetic tastes of consumers and weavers. Both textiles showcase the local plants, flowers and colour preferences. Islam influences Atlas textiles and hence without animal and figurative pattern. Patola has different patterns for consumers from different religious following. Atlas and Patola are the fabrics of inheritance and represent two different regions and cultures.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.049
GPT teacher head0.293
Teacher spread0.244 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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