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Architectural Design Styles and Case Studies from Different Countries

2024· article· en· W4401229020 on OpenAlexaff
Rui Zhang

Bibliographic record

VenueCommunications in Humanities Research · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsArchitectureCraftContext (archaeology)Architectural designArchitectural engineeringArchitectural patternArchitectural technologyComputer scienceEngineeringHistorySoftwareSoftware developmentArchaeology

Abstract

fetched live from OpenAlex

Through the appreciation and comparison of the various architectural styles of the East and the West, this paper studies the architectural form and structure produced in the context of different cultural differences and analyzes them in detail in various aspects. The essay takes the evolution of ancient and modern architectural styles of various countries in the East and West as the background of the study and explores the differences in architectural styles between different geographical countries and the factors that lead to these differences. Through the appreciation and analysis of typical architectural styles of these countries to compare the similarities and differences between the derivation of architectural styles, to explore the details of the characteristics of the architectural structure, to better derive the development trend of architecture and how to effectively help the future progress of architecture, which requires the mutual learning of the art and culture of each country, a joint effort. The exploration of architectural design requires more in-depth understanding and research, more refined through the integration of modern technology and traditional craft art.

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)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.012
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.519
GPT teacher head0.409
Teacher spread0.110 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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