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Record W7128511225 · doi:10.64903/1480-6800.24.4.265

Urban Landscape and Geography of the City in Islamic Traditions

2021· article· W7128511225 on OpenAlexvenueno aff
Naeema Al Hosani

Bibliographic record

VenueArab world geographer · 2021
Typearticle
Language
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsIslamSymbol (formal)Urban geographyUnderpinningHuman geographyPoliticsHistorical geographyArgument (complex analysis)Arabic

Abstract

fetched live from OpenAlex

Historically, the city as a geographical location and a symbol of stability as well as the meeting place of different races has occupied a paramount position in the writings of Arab geographers and travelers. This paper explores the geography of the Arab-Islamic city from different perspectives in order to underline the various elements underpinning the development of the urban landscape of the Arab city in different eras. Initially, the paper investigates the geography of the Arab-Islamic city in Arabic travel literature with focus on the travel chronicle of Ibn Jubayr. However, the predominant argument of the paper deals with the impact of Islamic conquests on shaping the urban landscapes of Arab-Islamic cities in ancient times. The paper argues that after the early Islamic conquests of other nations, the city constituted a driving force for Islam and Muslims built mosques in the heart of cities as a symbol of their religious and political power. The paper reveals that ancient cities in Islamic countries had a morphology that was the result of the needs of their residents. Moreover, the paper illustrates that in the current era the Arab-Islamic city has declined compared to the cities of the West, for many reasons, including random expansion as result of administrative corruption, in addition to political, economic, social and natural drawbacks.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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.

Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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