A Framework for E-Government Portal Development
Bibliographic record
Abstract
Electronic government (E-government) portals are considered one of the most popular conduits for offering government services online. Successful e-government portal development projects have been lauded in several academic and practitioner papers. These projects have concentrated on integrating government agencies by working to break the traditional silo-based view of the government and providing seamless integrated online services to citizens. However, the rate of adoption for e-government portals by citizens has been much lower than expected. A major reason identified in the literature for this is a lack of understanding of managerial considerations that affect portal development and subsequent adoption. In this chapter, we present a framework of managerial considerations for the development of e-government portals. The framework builds upon available literature in the field of e-government and public administration. It consists of eight key front-office and back-office considerations that contribute to successful development of an e-government portal. It provides an excellent platform for future research on e-government portals. The framework can also be extended to managers as a useful tool for ascertaining the effectiveness of their government portal development.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".