Bibliographic record
Abstract
Today, in many countries, looking for government information, filing taxes, renewing a driver’s license, obtaining a certificate and notifying of a new address anytime, anywhere are becoming mundane online operations. For the satisfaction of their constituents, local governments are striving to deliver more effective and efficient online services through the use of innovative information and communications technologies. E-government also known as “digital government” can be defined as the civil and political conduct of government using information and communication technologies (ICT) (McIver & Elmagarmid, 2002). The most accepted picture of e-government is that of a provider of online services to citizen (G2C), businesses (G2B) and the administration (G2G). The real value of an e-government rests on the effectiveness of its programs, the broad availability of its enhanced online services, the satisfaction of customers and the tangible savings in time, money and human resources (Koné, 2005). E-government expansion and adoption by communities, citizens, businesses, and public administrations in most countries is generally seen as a four-step process: presence phase, interaction phase, transaction phase, and transformation phase. The goal of the last transformation phase is to integrate several internal services at the vertical and horizontal levels, into a one-stop, whole-of-government with innovative services operating seamlessly across departments, agencies and programs. To address the problems of seamless integration and interoperability (D’Auray, 2001), some actors in e-government are experimenting with the semantic Web promoted by Tim Berners-Lee (Berners-Lee et al., 1999, 2001), Web service technologies (McIlraith et al., 2001) as well as service oriented architecture (SOA) as a means for achieving integration and inter-operation in the service transformation phase. Scope and Structure of the article This chapter aims at presenting the semantic Web technology applied to the transformation and advancement of e-government. After this introduction in the first section, we expose in the second section the nature of the semantic Web and e-government. Then, we explain in the fourth section, how semantic Web technologies can contribute to solving known issues in the transformation of e-government. Given this background, we are able to propose a simple illustration of our ideas: Web services and semantic Web-based architectures within the e-government project of Québec, Canada. We then give a glimpse of some future trends in the fourth section and the conclusion in the fifth and last section.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".