A Best Practice Based E-Government E-portals Quality model: A detailed view
Bibliographic record
Abstract
E-government portals are playing an important role in facilitating the citizens' life. The e-government services can be executed by citizens without any time and location constraints, which results in great benefits for them. Therefore, governments should pay a special attention to the quality of their e-government portals. In a previous work the ISO 25010 quality characteristics were mapped with the e-government portals' best practices. As a result a new e-government portals quality model based on ISO 25010 quality characteristics and the e-government portals' best practices was proposed. The aim of this paper is to present the detailed view of this quality model with its characteristics and sub characteristics. Such a quality model, will allow agencies to both; measure e-portals quality in a unified, reliable and easy manner, and identify the missing best practices that could improve the quality for those e-portals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".