Urban Safety is a Tool for Containing Slums to Reach a Sustainable Urban Structure
Bibliographic record
Abstract
Many cities suffer from the large spread of slums, especially the cities of the Middle East.The purpose of the paper is to study the reality of informal housing in Al-Barrakia and the most important problems that it suffers from.The paper also seeks to study the presence or absence of a correlation between urban safety indicators and urban containment indicators as one of the methods of developing and planning cities.This can be achieved through sustainable urban management.The slums are a source of many urban problems that threaten the security and safety of the residents and represent a focus for the concentration of crimes and drugs.The paper seeks to answer the following question: How can urban safety be improved through urban containment indicators?The research uses the descriptive analytical method by presenting urban problems related to slums and the most important indicators of slum containment to improve urban safety.Several indicators of urban containment were identified and classified into (community, physical, social, economic, politics).Influencing urban security within the economic and social dimension, the analysis was adopted through questionnaire, observation and statistical method.The paper concluded that there is a high correlation between urban containment indicators and urban safety, as the coefficient of determination R reached 94%.This means that the urban containment indicators explained 94% of Urban safety, The remaining percentage was explained by other indicators that were out of the scope of the present paper.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".