Evaluation of Egypt’s National Urban Policies and their Role in Addressing Urban Poverty
Bibliographic record
Abstract
The absence of Egypt’s adoption of clear urban policies has exacerbated urban development issues, including urban poverty. Drawbacks witnessed during the implementation of such policies emphasized the inevitability of their assessment within what Egypt is currently adopting as part of its national urban policy preparations. The research overviews the various aspects related to this issue based on analytical and descriptive methods to evaluate the Egyptian and selected worldwide experiences in this field, according to a set of principles and criteria that contribute to drawing conclusions to deal with the repercussions of this phenomenon and its impact on urban and economic dimensions, etc., In order to formulate basic considerations for the formulation of Egypt's urban development policies to deal with urban poverty, to ensure the formulation of adaptive mechanisms and urban governance. Future research in this article will examine how to achieve coordination and complementarity among the concerned organizations in the formulation of urban policies in order to overcome the constraints related to the implementation and implementation of policies. One of the most important innovations in this area is the initiation of work on the formulation of urban policies on specific development issues such as urban poverty... and other issues and challenges facing Egypt's urban development.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".