Bibliographic record
Abstract
Electronic government (e-government) initiatives are pervasive and form a significant part of government investment portfolio in almost all countries around the world. However, understanding of what is meant by e-government is still nascent and becomes complicated because the construct means different things to different people. Consequently, the conceptualization and implementation of e-government programs are diverse and are often difficult to assess and compare across different contexts of application. This paper addresses the following key question: Given the wide variety of visions, strategic agendas, and contexts of application, how may we assess, categorize, classify, compare, and discuss the e-government efforts of various government administrations? In answering this question, we propose a generic e-government framework that will allow for the identification of e-government strategic agendas and key application initiatives that transcend country-specific requirements. In developing the framework, a number of requirements are first outlined. The framework is proposed and described; it is then illustrated using brief case studies from three countries. Finally, findings and limitations are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".