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Record W2911268932 · doi:10.1177/2158244019825604

An Urban Code in Traditional Middle Eastern Contexts: The Edge Environment as a Central Theme for Reading the Social Pattern Language of Historic Sites

2019· article· en· W2911268932 on OpenAlexaff
Gamal Mohammed, Noha Mahmoud

Bibliographic record

VenueSAGE Open · 2019
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsDurham CollegeOntario College of Art and Design
Fundersnot available
KeywordsContext (archaeology)IdeologyReading (process)Code (set theory)Theme (computing)Sense of placeSociologyBuilt environmentArchitectural engineeringUrban planningGenerative grammarAestheticsComputer scienceCivil engineeringLinguisticsGeographyEngineeringSocial sciencePolitical scienceArchaeologyWorld Wide WebArtificial intelligenceArt

Abstract

fetched live from OpenAlex

This article discusses a new concept that may help professionals and specialists read the “urban code” of Middle Eastern traditional contexts that was developed from the mix of social aspect and spatial morphology, illustrating how these elements are interconnected in a way that highlights the values and qualities and their reflections on the physicality of the city. This urban code envisions and analyses the relevance of the social pattern language of the traditional context to its urban manifestation, leaning on the “edge environment” as a new generative concept. It outlines the relationship between the ideologies buried underneath the walls of the spatial form of traditional built environment such as Cairo and sheds light on those ideologies in a way that helps us read them within the context of modern values pertained to the sense of community. The notion of the edge environment may contribute to design education restoration, preservation, and upgrading processes as design toolkit that employs careful interventions by fine-tuning the edge environment.

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 score0.017
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.036
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0010.002
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 head0.030
GPT teacher head0.230
Teacher spread0.199 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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