Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development
Bibliographic record
Abstract
In response to economic, political, and technological stimuli, governments across the globe are progressively embracing digital transformation to devise innovative digital solutions.Despite these advancements, challenges persist in the integration of information resources, including deficiencies in government information systems and threats to network and information security.This paper investigates a novel algorithm for the filling and classification of big data within E-government systems, which comprises data management and governance, cultural and industrial shifts tied to human resource development, and data exchange protocols.A cloud computing environment serves as the infrastructure for constructing an E-government big data intelligence system.The system enables parallel data processing and classification via decision trees, thereby promoting the efficacious and sustainable employment of big data analytics in policy formulation and digital innovation.Additionally, the paper delineates the hurdles and issues that confront these agencies, and proposes potential solutions to augment citizen satisfaction and to deliver value within and beyond governmental sectors.The findings suggest that the integration of big data technologies in E-government presents an effective strategy for the provision of interactive services, thereby addressing citizens' demands for enhanced services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".