AN ASSESSMENT STUDY OF UNPLANNED SETTLEMENTS CASE STUDY: AL-RUWAIS DISTRICT, JEDDAH, SAUDI ARABIA
Bibliographic record
Abstract
Saudi Arabia is a large country that includes many large cities; the second largest city is Jeddah. Like many other cities worldwide, Jeddah contains significant areas that were established as unplanned settlements. According to the definition of the United Nations (UN) many of Jeddah’s unplanned settlements have been classified as slums. However, some of these unplanned settlements have strategic location near the center of Jeddah, and present great potentials as a new urban quarter. Four distinctive areas were the subject of the upgrading studies conducted by Jeddah Municipality. These are Khozam district, Bani-Malik district, Al-Boghdadiyah District, and Al-Ruwais district. This study focuses on assessing the physical condition of the built environment of Al-Ruwais district as well as to investigate the common problems of the residents such as buildings problems, infrastructure problems, and social problems. It also intends to give an overview for the suggested developing programs for the rebuilding of Al-Ruwais district by the Jeddah Municipality. The main objective of the study is to identify ways in which interventions aimed at delivering services to the low-income residents can be better designed and targeted, as well as to define set of recommendations for future unplanned settlements upgrading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".