The role of e-Government in overcoming the consequences of the COVID-19 pandemic in Nigeria
Bibliographic record
Abstract
Abstract: Purpose: The article aims at identifying the challenges of e-government amid the COVID-19 pandemic in Nigeria and proffered recommendations to arrest the identified challenges. This paper also examined e-Governance in selected countries such as the United States of America, the United Kingdom, and Canada and how it has fared including Nigeria revealing its implications for Nigeria as a developing nation. Research Methodology: The article adopts a review study approach in analyzing the subject. Results: Some of the challenges identified by the study include but are not limited to inadequate technical know-how and ICT skills to drive and sustain e-government. Recommendations from the study include, the Ministry of Communications Technology and Digital Economy to build a backbone that will connect all States of the country and the upskilling of the workforce through the Ministry of Labour and Employment amongst others. Limitations: Insufficient quantitative data based on the subject under discourse Contributions: Identified possible areas that the Nigerian government could look into to improve e-government in order to promote inclusivity, awareness, and most importantly reduce the cost of governance. Keywords: 1. COVID-19 2. Digital Solutions 3. e-Governance 4. ICT
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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 | 0.005 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".