Toward a Roadmap to E-Government for a Better Governance Toward a Roadmap to E-Government for a Better Governance
Bibliographic record
Abstract
Information and Communication Technologies (ICTs) have a tremendous potential to improve the quality of people’s livelihood in general and especially in the developing countries. They can boost business, support education and health systems and also enhance the governance that is a major and vital factor in the development process. It is commonly agreed that e-Government systems enhance governance, but, unfortunately, there is a lack of empirical evidence to build upon this hypothesis, which, legitimately, creates reluctance among key decision makers, slows down the dissemination of technology as a decision support tool and as development enabler/infrastructure and contribute to the very dangerous phenomena known as the digital divide. In the context of Fez e-Government Project, that is being led in Morocco, in a close collaboration with the municipality of the Moroccan city of Fez, authors have developed a pilot e-Government system that facilitates citizens’ access to governmental information and services. From the outset of this 30 months project, the goal was to collect and analyze experimental data in order to see how the development/deployment of e-government systems impacted the governance process. This research has set up a methodology that emphasizes good governance at each step of an e-Government project and enables the researchers to continuously assess the outcomes of the resulting e-Gov system on governance. The ultimate goal is to reduce, as much as possible, the reluctance of politicians and decision makers and to contribute in the dissemination of technology for development purposes thru a scientific and proven methodology that systematically links e-Government outcomes to good governance attributes. In this chapter, authors present the main phases of this methodology and lessons learned during the e-Fez Project. This approach may benefit similar projects, especially in developing countries that are willing to create and deploy e-Government systems for the benefit of their citizens.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".