A DECADES-OLD DREAM: TOWARDS BRANDING THE UN-NAMED CITY OF THE NEW ADMINISTRATIVE CAPITAL IN EGYPT
Bibliographic record
Abstract
In confronting the intensified globalization in today’s world. Cities, the economic engines and cultural hearts of nations are in quest for creating positive images that allow them to achieve multi-dimensional uprising that will help them get their portions of the world’s tourists, talents, businesses, investment, funds, reputation, respect and attention. Therefore, city branding has become a vital need; as it attempts to give each city its distinctive image and identity. Nowadays, The New Administrative Capital (NAC) is reshaping Egypt’s future thoroughly. Nevertheless, research on its branding is lacking. Building on qualitative research, this paper aims at developing a brand for the NAC based on examining how specific target group perceive its image, and how to benefit from their perception in the branding of the NAC. The results highlight the importance of mega urban projects in creating the city image, the key role of social media in delivering that image, and suggests a positioning statements and a message that can be employed in the communication materials through which the brand identity would be communicated. This study contributes to the rapidly growing domain of city branding by exploring the perception of architects, urban planners in the context of branding new cities. And since similar studies do not exist in literature, this study fills an important research gap.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".