Nigeria: Government Covid-19 Interventions to Promote Inclusive Adaptation and Economic Recovery
Bibliographic record
Abstract
The Covid-19 pandemic has been a major and global public health challenge. Like every other country, Nigeria has suffered huge human and economic losses. About 87,607 cases of Covid-19 and 1,289 deaths had been reported by 31 December 20201. The Nigerian economy shrank by 1.8% in 2020, mainly as a consequence of the effects of the pandemic. In addition, the unemployment rate rose from 23.1% in the third quarter of 2018 to 27.1% in the second quarter of 2020, according to the National Bureau of Statistics (NBS). Different sections of Nigerian society were affected in different ways. In particular, the informal sector and small and medium-sized enterprises (SMEs) were the most affected, as well as poor households (NBS, 2021). The pandemic also had a disproportionate impact on women (UN, 2020). To mitigate the negative economic effects of the pandemic, the Nigerian Government implemented monetary and fiscal policies, as well as income support policies and programmes to safeguard the most vulnerable economic groups. These interventions translated to increased government expenditure, a decline in government revenue (as a result of lower demand for crude oil exports) and a growth in the government budget deficit and public debt.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".