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Record W4409061999 · doi:10.5539/ass.v21n2p37

Research on the Design of Tang Dynasty Hunting Pattern Cultural and Creative Products Based on Narrative Design

2025· article· en· W4409061999 on OpenAlexvenueno aff
Xiangyang Bian, Lingyi Zeng

Bibliographic record

VenueAsian Social Science · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural and Communication Design Research
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAestheticsArchitectural engineeringBusinessSociologyHistoryLiteratureArtEngineering

Abstract

fetched live from OpenAlex

The hunting pattern is a popular style of Tang Dynasty textiles, which reflects the social culture and aesthetic interest of the time and is a witness to the fusion of Chinese and foreign cultural exchanges. From the perspective of narrative design, this paper takes the Chinese traditional hunting spirit as the core and cultural and creative products as the final expression, to realize the innovative design application of Tang Dynasty textile patterns. From the analysis of the three narrative elements of the Tang Dynasty hunting pattern, namely, narrative theme, plot, and expression level, the narrative design is developed by combining the product shape, material, and color. The design emphasizes the fun of the product and the interaction with the users to promote the users' in-depth experience and cognition of traditional Chinese culture, which provides a methodological guide for the practice of narrative design in cultural and creative products.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0000.001
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 head0.279
GPT teacher head0.403
Teacher spread0.124 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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