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Record W7119026253 · doi:10.18280/ijsdp.201118

A Proposed Metropolitan Governance Model for Strengthening the Emerging Metropolis of Port-Said in Egypt

2025· article· W7119026253 on OpenAlexvenueno aff
Marwa Tharwat Elmoazamy, Randa Ali, Mona Abdel-Fatah Abdel-Moneim, Walid Bayoumi

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Language
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaCorporate governanceUrban planningGovernment (linguistics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.270
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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