E-Government Portals Maturity Models: A Best Practices’ Coverage Perspective
Bibliographic record
Abstract
E-government is a field where oriented practice is considered crucial for its prosperity.Therefore, best practices are considered among the success factors of e-government portals.To this end, e-government maturity models can be used to provide guidance and guidelines to identify those best practices.After an extensive literature review, we have collected both; the e-government portals' best practices and organized them according to their purposes in an e-Government Portals' Best Practice Model (eGPBPM), and the set of 25 maturity models best practices in two separated previous published studies.The eGPBPM is composed of four best practice categories including: back-end, Web design, Web content and external.Moreover, each maturity model has several stages of maturity and each stage include a set of best practices used to rank the maturity of e-government portals.The goal of this paper is to identify the extent to which e-government maturity models are covering the best practices of the eGPBPM.To achieve this goal, a mapping between the maturity models' best practices for each maturity stage and the best practices of the eGPBPM has been performed.Our findings show that although this set of maturity models are used in practice, they include only some of the e-government portals' best practices and none of them have a full coverage of those best practices.
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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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".