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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".