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Record W3215630671 · doi:10.5539/ass.v17n12p55

Research on Art Intervention in Rural Design Based on the Cultural Ecology: A Case Study of the Xun Jiansi Village in Jiangxi Province, China

2021· article· en· W3215630671 on OpenAlexvenueno aff
Mengqi Wang

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCultural ecologyPromotion (chess)Intervention (counseling)Inheritance (genetic algorithm)Plan (archaeology)Cultural inheritanceRural areaSociologyEconomic growthEcologyGeographyPolitical sciencePsychologyEconomicsAnthropology

Abstract

fetched live from OpenAlex

In 2021, the world will enter the post-epidemic era. China is at the end of the 13th Five-Year Plan for the integration of urban and rural development and rural revitalization. At the same time, the internal circular economy brings opportunities for rural development and regional revitalization. This article uses interdisciplinary research methods, integrates various design models involved in cultural ecology, and strengthens the connections between them, combined with innovative thinking in rural design, art promotion, cultural inheritance, and industrial upgrading paths. The article intends to solve how to use environmental art and public Art, experimental art and other art forms are involved in rural design, and based on the theoretical framework of cultural ecology, the rural design method is constructed through the case analysis of the Xun Jiansi village, and the innovative and characteristic development path of art intervention in rural design is studied.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.007
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
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 head0.060
GPT teacher head0.378
Teacher spread0.317 · 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
Published2021
Admission routes1
Has abstractyes

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