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Record W4308898154 · doi:10.5539/jsd.v15n6p79

Towards a Comprehensive Approach for Sustainable Neighborhood -The Sudanese Context

2022· article· en· W4308898154 on OpenAlexvenueno aff
Z. E. Awad

Bibliographic record

VenueJournal of Sustainable Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityGeographyEnvironmental planningContext (archaeology)Sustainable developmentLand useDiversity (politics)Urban planningEnvironmental resource managementSociologyEcologyEconomics

Abstract

fetched live from OpenAlex

This paper analyzed the main factors of sustainable neighborhoods to evaluate some of the residential neighborhoods in Sudan. The main objective of this study is to highlight the importance of a comprehensive framework for assessing sustainable neighborhood developments in Sudan. Four neighborhoods in Khartoum city were selected as case study areas with socio-spacial diversity. The analysis of the neighborhoods was based on land-use profile and field observations compared with UN-Habitat principles of the sustainable neighborhood which include: the design of street networks, high density, mixed land-use, social mix, and limited land-use specialization. The research examined the current situation in these neighborhoods and their potential to become sustainable in the future. The paper found that these neighborhoods are not fully sustainable and self-contained each selected neighborhood has some sustainable principles. The analysis showed that other influential factors contributing to urban sustainability are ignored by UN-Habitat principles such as the planning pattern of the area and the inhabitants' way of living. The paper presents a comprehensive framework to assess sustainable development in neighborhoods that include in addition to the above-mentioned urban parameters other factors such as location and distance from the town center, isolation from workplaces, and accessibility to a higher level of social services.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.273
Teacher spread0.241 · 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 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
Published2022
Admission routes1
Has abstractyes

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