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Record W1491145661

Habitant’s Empowerment of urban timeworn textures along with improvement and renovation process: (Case sample: Qal’eh quarter of Dezful)

2015· article· en· W1491145661 on OpenAlexaboutno aff
Saeedeh Rashidiasl, Zohreh Ezzati, Mostafa Rashidiasl

Bibliographic record

VenueDergiPark (Istanbul University) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)EmpowermentSample (material)Process (computing)BusinessEngineeringComputer scienceGeographyChemistryPolitical scienceArchaeologyChromatography
DOInot available

Abstract

fetched live from OpenAlex

Abstract. Today, urban timeworn textures are of the most important problems which cities are facing with. These textures are mainly parts of the city which are separated from evolutionary cycle and are formed as center of difficulties and inadequacies. These parts have lost livability during time and changing space patterns of construction and have many problems in terms of skeletal conditions, traffic, economic and social for modern life.Dezful is one of the cities suffering from timeworn textures among quarters of the city. We can point to Qal’eh quarter which is one of the oldest quarters of Dezful. Therefore, aim of this study is to recognize problems of timeworn and proposing appropriate solutions to empower habitants of this texture in order to renovation and improvement. Method of research is descriptive, analytical and surgery with a theoretical–participatory procedure. Data collecting method is in the form of library field and questionnaire about studied quarter it is dealt with feasibility of participation in improvement of timeworn texture of the quarter using supervisor of family questionnaire and some strategies were extracted to propose solution for improving and renovating this quarter.

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)0.000
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 score0.391
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.200
Teacher spread0.184 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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