Bibliographic record
Abstract
The U.S. government is making strides to provide electronic access to government agencies and services. A variety of issues are involved when implementing e-government programs such as electronic tax filing, access to drug information, and so forth. Financial, technical, personnel, and legal issues are common. Privacy issues in the creation of e-government are also of interest to both the e-government implementer and citizen. There are a variety of issues in planning and implementing projects of the scope and magnitude of e-government. Issues such as user requirements, organizational change, government regulations, and politics, as well as descriptions of planning and implementation frameworks, are important. Experience in developed countries shows that it is not difficult for people to imagine a situation where all interaction can be done 24 hours each day, 7 days each week. Many countries, including the United States, France, Australia, Greece, Canada, Singapore, and Italy have been offering government services online (West, 2004). According to Sharma and Gupta (2003), Canada, Singapore, and the United States are categorized as “innovative leaders” (p. 34) whose continued leadership in the creation of e-government and more mature online services sets them apart from other countries. Canada leads the way in e-government innovation while Singapore, the United States, Australia, Denmark, the United Kingdom, Finland, Germany, and Ireland are countries in the top-10 list. Several Asian countries such as China, Hong Kong, India, Japan, Philippines, Indonesia, Thailand, Bangladesh, and Burma have initiated the concept of e-government as well (Dodgson, 2001). An article in Federal Computer Week (Perera, 2004) reported findings of a recent poll indicating that 77% of Internet users (or some 97 million people) in the United States have gone online for government information. E-government is rapidly becoming a key priority of the government of the United States.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".