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Record W4413543027 · doi:10.18280/ijsdp.200715

Sustainability Index Mapping for Green Building of Vertical Housing in Ibu Kota Nusantara (IKN) Using the Multidimensional Scaling (MDS) Approach

2025· article· en· W4413543027 on OpenAlexvenueno aff
Iwan Suprijanto, Moh. Khusaini, Emiliya Antariksa, Anthon Efani

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMultidimensional scalingSustainabilityIndex (typography)ScalingEnvironmental scienceBusinessComputer scienceMathematicsStatisticsGeometryEcology

Abstract

fetched live from OpenAlex

The study aims to assess the sustainability status of green buildings for vertical housing in Ibu Kota Nusantara (IKN).It responds to growing environmental, social, economic, infrastructure, and institutional challenges driven by rapid urban development.Using the Multidimensional Scaling (MDS) approach, combined with Monte Carlo simulations, the research evaluates five key dimensions of sustainability: environment, economy, social, infrastructure, and institution.Data were collected from expert respondents through purposive sampling and analyzed using leverage and stress value validation.The findings reveal that the vertical housing model in IKN falls within the highly sustainable category, with strong performance across all assessed dimensions.The study emphasizes the role of green building practices in supporting smart city goals, reducing carbon emissions, and enhancing urban livability.Results are expected to inform policy decisions and contribute to the formulation of effective sustainability strategies in large-scale urban development.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.284
Teacher spread0.246 · 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 designSimulation or modeling
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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