E-Government Readiness Assessment for Government Organizations in Developing Countries
Bibliographic record
Abstract
ICT has become an increasingly important factor in the development process of nations. Major barriers can be met in the adoption and diffusion of e-government services depending on the readiness of a country in terms of ICT infrastructure and deployment. This study aims to define organizational requirements that will be necessary for the adoption of e-government to resolve the delay of ICT readiness in public sector organizations in developing countries. Thus, this study contributes an integrated e-government framework for assessing the ICT readiness of government agencies. Unlike the existing e-government literature that focuses predominantly on technical issues and relies on generic e-readiness tools, this study contributes a comprehensive understanding of the main factors in the assessment of e-government organizational ICT readiness. The proposed e-government framework comprises seven dimensions of ICT readiness assessment for government organizations including e-government organizational ICT strategy, user access, e-government program, ICT architecture, business process and information systems, ICT infrastructure, and human resource. This study is critical to management in assessing organizational ICT readiness to improve the effectiveness of e-government initiatives.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".