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Record W2902383470 · doi:10.5539/jsd.v11n6p70

Partitioning (Facade) and Identity in the Historical Context Case of Zonouz City

2018· article· en· W2902383470 on OpenAlexvenueno aff
Hassan Khalili Zonouz

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)OriginalityVitalityContext (archaeology)Place identitySociologyArchitectureFacadeEpistemologyAestheticsEconomic geographyUrban planningRegional scienceGeographySocial scienceCivil engineeringArchaeologyArtEngineering

Abstract

fetched live from OpenAlex

City, identity, and urban landscape are interconnected with each other. Urban restoration is the final objective of the study and implementation of partitioning in historical contexts. The identity of city and its determining urban elements were always discussed while the urban passages are the most prominent elements in creating the historical context of urban landscape. The present study aimed at investigating the implementation of favorable partitions by considering vitality in historical contexts. This study analyzed the identity, originality, and partitioning to apply the principles defining a logical solution and creating the sense of belonging, collective memories, sense of place, and points related to environmental psychology in the historical contexts. Using some landscape architecture principles by considering some parameters such as originality, identity, and social-cultural elements can contribute to idealizing the identity partitioning to historical contexts and its adjacent points in urban areas. As a result, the identity concepts and parameters can interpret the partitions in terms of thought, design, and implementation of the principles used in the architecture.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.340
Teacher spread0.304 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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