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Peculiarities of Adaptive Reconstruction of Buildings and Their Potential for New Functions (Using Religious Buildings and Structures as a Case Study)

2025· article· en· W4414894197 on OpenAlexaboutno aff
B.Zh. Karpseitova, S.Sh. Sadykova

Bibliographic record

VenueBulletin of L N Gumilyov Eurasian National University Technical Science and Technology Series · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)RestructuringExhibitionArchitectureEveryday lifeCultural heritage

Abstract

fetched live from OpenAlex

This article presents an analysis of the adaptive restructuring of religious buildings and structures, taking into account their new functional capabilities, with examples drawn from international experience. The main objective and concept of the study are focused on analyzing completed projects of the adaptation of religious buildings carried out by contemporary architects and engineers. The works and approaches discussed in this article can be utilized by architects, engineers, restorers, and urban planners when planning projects for the restoration, reconstruction, and repurposing of religious buildings and structures. The study includes examples of the adaptation of Christian church buildings in countries such as the United States, Canada, the United Kingdom, France, the Netherlands, and others. The research demonstrates how historic buildings can be successfully adapted to meet contemporary needs while preserving their historical and cultural heritage, which is crucial for the sustainable development of cities. The adaptation of church spaces for new functions (such as museums, exhibition halls, concert halls, libraries, etc.) provides opportunities for social interaction, cultural exchange, and economic development. The study deepens the understanding of the transformation of sacred spaces and their integration into everyday life, thus expanding the boundaries of architectural theory.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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