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Record W4389915591 · doi:10.3390/buildings13123136

Revealing a Gap in Parametric Architecture’s Address of “Context”

2023· article· en· W4389915591 on OpenAlexafffund
Morteza Hazbei, Carmela Cucuzzella

Bibliographic record

VenueBuildings · 2023
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArchitectureConnotationContext (archaeology)PoliticsParametric designArchitectural engineeringSociologyParametric statisticsComputer scienceEpistemologyEngineeringPolitical scienceGeographyLawLinguisticsMathematics

Abstract

fetched live from OpenAlex

“Context” holds a broad meaning in architectural discourse, and its definition and components have evolved over time. A comparison between contemporary parametric design and overall architectural practices reveals a contradictory connotation of context in these discourses. In parametric design, as it is currently practiced, the concept of “context” appears to have shifted primarily toward energy considerations and quantifiable parameters, neglecting the broader range of site forces. However, it raises the question of whether parametric design can still be considered contextual and sustainable design when it overlooks compatibility with broader contextual dimensions such as cultural, social, and historical forces. To answer this question, we establish a clear and comprehensive definition of “context” in overall architectural practices by exploring the different meanings and epistemologies of “context” in cultural, social, historical, physical, environmental, political, and economic domains. This process helps us determine which context components can be incorporated into parametric architecture and which cannot, thereby aiding in the integration of sustainability principles into parametric design. The results show that while physical and environmental components can be included in parametric architecture, intangible parameters such as cultural, historical, social, economic, and political aspects cannot be easily quantified and thus are difficult to incorporate.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.020
Scholarly communication0.0110.014
Open science0.0010.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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 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

Citations12
Published2023
Admission routes2
Has abstractyes

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