Planning of Urban Quarters, Districts and Neighbourhoods in Islam’s Holiest City: A Comparative Analytical Study
Bibliographic record
Abstract
Global experiences of planning urban areas are characterised by adopting hierarchical structures from large to small structures, i.e. residential quarter (large residential area), residential district and then residential neighbourhood. Each structure is characterised according to the area, the number of people and the services required for targeting a sustainable urban environment. This concept is a milestone in urban planning in Saudi Arabia. The research attempted to discuss the general trend of planning housing projects in Saudi Arabia and the extent of applying the hierarchal concept. Three models were chosen randomly in Makkah, showing such planning hierarchy. These are Al Shawqiyyah, Al Sharaie and Al Rabwah. These projects were planned four decades ago and became a reality. Through comparative analytical methods, findings revealed ambiguity of the hierarchal planning concept in these projects. Further, the traditional neighbourhood concept was prevalent in most neighbourhoods characterised by low-density development in planning but medium and high densities in reality. The problem is getting worse as cities grow horizontally and vertically. Consistent scientific analysis based on practice and continuous meta-analysis was used. The paper calls for the need to change the current premises of housing plans and reorganise the accumulated problems according to global experiences and local standards.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".