Influences on e‐governance in Africa: A study of economic, political, and infrastructural dynamics
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
Abstract E‐governance is considered one of the most important factors in delivering and administering public services in modern societies. However, data show that many African countries are currently lagging behind countries in other parts of the world. This manuscript investigates how various factors, including economic prosperity, government effectiveness, and infrastructural support, contribute to the growth and effectiveness of e‐governance initiatives in 54 African countries. We specifically analyze the influence of three factors: economic prosperity (measured by GDP per capita), political competence (measured by government effectiveness), and infrastructural or technological support (measured by access to electricity). Panel data covering a 5‐year period were retrieved from databases of the United Nations and World Bank, and a multiple linear regression analysis was used to analyze the data. We found that the three factors influenced e‐governance to varying degrees. However, while infrastructural support and political competence were statistically significant, economic prosperity was not.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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