Trends in E-government Services: The Case of Dubai City, United Arab Emirates
Bibliographic record
Abstract
The high-level strategic planning of the governments worldwide aims to build citizen’s trustworthiness and increase the operational efficiency, which can be partially achieved by means of Electronic Government (e-government). This concept becomes a mandatory element in smart city architecture, including other related evaluation parameters such as technology, social engagement, etc. This paper addresses the impact of different factors in evaluating the e-government services and their development in smart cities. A proposed framework is designed based on the key significant parameters in the literature, as there is no standard operational frameworks in e-government state-of-the-art. This study analyzes the Smart City Ranking and Digital Governance data provided in the Municipalities Worldwide Survey (DGMWS) by focusing on Dubai city as a case study in order to better understand the evaluation parameters for e-government services in smart cities. The findings indicate four significant key factors for the e-government operation cycle in smart cities that lead to sustainable development: stakeholders’ participation, budget, service development, and integration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".