A Proposed Metropolitan Governance Model for Strengthening the Emerging Metropolis of Port-Said in Egypt
Bibliographic record
Abstract
Egypt's national urban system has experienced a significant urban shift, with the development of 14 metropolises in 2018, expected to reach 17 in 2030, according to the UN World Urbanization Prospects (WUP).Given the diversities in the spatial structure, urbanization level, population, and economic size of the Egyptian metropolises, no urban governance model has been envisaged to fit the metropolises' urban dynamics.Based on the fact that no metropolitan governance model fits all, the research aims to determine the most appropriate metropolitan governance model for addressing the complexity of Port-Said Emerging Metropolis (PSEM) current issues, as one of Egypt's emerging and promising metropolises.The research methodology included a comparative study of the different theoretical conceptions and the metropolitan governance models.Next, it established the analytical framework to assess the appropriateness of every model.Then, a partially closed-ended questionnaire was conducted to assess the appropriateness of the different governance models for the PSEM.The research's findings and recommendations emphasize the importance of the Council of Governments model as an effective metropolitan governance model for managing the challenges of the emerging metropolis of Port-Said and enhancing efficient and sustainable development to achieve its development objectives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".