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Record W2993200018 · doi:10.3390/su11246898

Cultural Sustainability and Vitality of Chinese Vernacular Architecture: A Pedigree for the Spatial Art of Traditional Villages in Jiangnan Region

2019· article· en· W2993200018 on OpenAlexafffund
Qi Liu, Zaiyi Liao, Yongfa Wu, Dagmawi Mulugeta Degefu, Yiwei Zhang

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsVernacular architectureVernacularVitalitySustainabilityChinaResource (disambiguation)ArchitectureGeographyUrbanizationArchitectural engineeringSustainable developmentConsumption (sociology)Civil engineeringEnvironmental resource managementSociologySocial scienceEconomic growthEngineeringArchaeologyComputer sciencePolitical scienceEcologyArt

Abstract

fetched live from OpenAlex

Presently, the rapid urbanization in contemporary cities in China has resulted in more buildings of low cultural value and high energy consumption. Many traditional Chinese villages exhibit special spaces that have been optimally adapted to the climatic and environmental features of the area using vernacular methods. The buildings in these villages can maintain the environment more sufficiently for the intended programs and consuming a lower level of resources. The construction technics and the artistic features in these spaces are invaluable and inspiring for contemporary architectural practices. This study aims to establish a pedigree of the artistic features exhibited in traditional Chinese villages to support sustainable development. This is to be achieved through thoroughly exploring the spatial design of these villages archived in a big-data resource. The pedigree integrates the dynamics (cultural changes over a certain period of time) and static (spatial features at a fixed time) of how the spaces in these villages have evolved. It is concluded that both a high level of sustainability and exceptional artistic quality have been achieved over a long history in many of these villages where traditional construction methods and design principals were employed.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.250
Teacher spread0.212 · 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
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

Citations72
Published2019
Admission routes2
Has abstractyes

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