A Base of Knowledge, Mobile, and Web 2.0 Technologies for Connected E-Government
Bibliographic record
Abstract
E-Government is an evolving field with continually changing practice and priorities. It is also a global phenomenon, from the richest and most technologically developed nations to the poorer and less technologically developed countries, involving a range of latest Information and Communication Technologies (ICT) and diverse methodologies. In such a dynamic field spanning all sectors of the governments and societies, it is difficult for e-government researchers and practitioners to identify the trends in the e-government activity and learn from previous cases and experiences. In this context, the aim of this chapter is to present an in-depth evaluation of e-government practice and research since 2007, to provide insight on research practicalities and emerging issues in e-government activity, and to identify the trends and technologies. The chapter also focuses on the current mobile and Web 2.0 technologies and examines the practicalities of using mobile technologies in various countries such as USA, Canada, UK, Austria, Japan, and others, as well as the practicalities of Web 2.0 technologies in some domains such as government, regulation, cross-agency cooperation, law enforcement, etc. This chapter presents a framework based on the mobile and Web 2.0 technologies in the context of e-government activity. In addition, the authors propose a framework for a government-people relationship. We hope to make a contribution for researchers, practitioners, policy makers, and people interested in e-government by providing a base of the e-government domain knowledge, practice, and framework. Additionally, the chapter illustrates how the implementation of mobile and Web 2.0 technologies support connected e-government.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".