The impact of COVID- 19 pandemic on the Nigerian economy; A case study of the financial sector
Bibliographic record
Abstract
The COVID-19 impact on economic performance has attracted a lot of attention among policy maker’s stakeholders and the academic. This study was circumscribed to a discussion on the impact of the COVID-19 pandemic on the Nigerian economy with a particular focus on the financial sector. Adopting reliable secondary data, the study adopted the ex post facto research design to evaluate the impact of the pandemic on the banking institutions, the insurance industry, and the stock market in Nigeria. The result of the analysis indicates that the banking sector has been experiencing low profitability and downsizing to contain the increased cost resulting from the pandemic, whereas the insurance firms are losing revenue sources due to the reduction of premium from the major sectors that subscribe to insurance packages. The stock market decline in market share and low market capitalization are indicative of the negative impact of the COVID-19. Pragmatic and effective policy responses are required by the government to reduce the proliferation of the virus and hence foster growth in the financial sector.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".