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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".