Towards a Comprehensive Approach for Sustainable Neighborhood -The Sudanese Context
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".