E-Government Portals Maturity Models: A Best Practices’ Coverage Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it