Design of Government Information for Access by Wireless Mobile Technology
Bibliographic record
Abstract
As the world becomes mobile, the ability to access information on demand will give individuals a competitive advantage and make them more productive on the job and in their daily lives (Satyanarayanan, 1996). In the past, government information was presented by government employees who verbally communicated with citizens in order to meet their information needs. As print technology improved, government information was, and still is in many countries, communicated to citizens using paper as the medium of delivery. Because of the cost of printing and mailing printed documents and the difficulty of updating information in a timely manner, governments are moving to electronic delivery of information using the Web. Currently, governments provide digital service to their citizens using the Web for access by desktop or notebook computers; however, citizens of many countries are using mobile devices such as cell phones, tablet PCs, personal digital assistants, Web pads, and palmtop computers to access information from a variety of sources in order to conduct their everyday business and to communicate with each other. Also, wearable mobile devices are being used by some workers for remote computing and information access in order to allow multitasking on the job. It is predicted that there will be more mobile devices than desktop computers in the world in the near future (Schneiderman, 2002). The creation of digital government will allow the delivery of government information and services online through the Internet or other digital means using computing and mobile devices (LaVigne, 2002). Also, there will be more government-to-citizen and government-to-business interactions. Digital government will allow citizens, businesses, and the government to use electronic devices in order to communicate, to disseminate and gather information, to facilitate payments, and to carry out permitting in an online environment (Wyld, 2004). Digital government will allow citizens to access information anytime and anywhere using mobile and computing devices (Seifert & Relyea, 2004).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".