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Record W2884530980 · doi:10.5539/ach.v10n2p45

The Impact of Cultural Diversity on Mosques in Malaysia

2018· article· en· W2884530980 on OpenAlexvenueno aff
Mansoureh Ebrahimi, Kamaruzaman Yusoff

Bibliographic record

VenueAsian Culture and History · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
FundersMinistry of Education, India
KeywordsDiversity (politics)DocumentationFunction (biology)ArchitectureArchitectural engineeringIslamSpace (punctuation)Islamic architectureSociologyPerspective (graphical)Work (physics)AestheticsEngineeringHistoryComputer scienceArchaeologyAnthropologyArtArtificial intelligence

Abstract

fetched live from OpenAlex

According to Malaysia Town and Country Planning Guideline and Standards (2002), two major considerations related to mosque usage and management are sufficient areas for both building and adjacent open space. As a sacred place for prostration to Almighty God, individually or in groups, mosque architecture has evolved considerably, from very simple designs and functions to more sophisticated forms and layouts. In Malaysia, various races have significantly influenced mosque design and function. The present work describes this evolution in terms of well-known mosques via qualitative observations and documentation, from earliest to latest architectural developments. Our findings demonstrate that architectural evolution and/or transformation did not alter the mosque’s main function from an Islamic perspective. Nonetheless, designs and structure did benefit usage, to include the attraction of tourists.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0000.003
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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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