Nigeria: Government Covid-19 Interventions to Promote Inclusive Adaptation and Economic Recovery
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
The Covid-19 pandemic has been a major and global \npublic health challenge. Like every other country, Nigeria \nhas suffered huge human and economic losses. About \n87,607 cases of Covid-19 and 1,289 deaths had been \nreported by 31 December 20201. The Nigerian economy \nshrank by 1.8% in 2020, mainly as a consequence of the \neffects of the pandemic. In addition, the unemployment \nrate rose from 23.1% in the third quarter of 2018 to 27.1% \nin the second quarter of 2020, according to the National \nBureau of Statistics (NBS). \n \nDifferent sections of Nigerian society were affected in \ndifferent ways. In particular, the informal sector and small \nand medium-sized enterprises (SMEs) were the most \naffected, as well as poor households (NBS, 2021). The \npandemic also had a disproportionate impact on women \n(UN, 2020). \n \nTo mitigate the negative economic effects of the pandemic, \nthe Nigerian Government implemented monetary and \nfiscal policies, as well as income support policies and \nprogrammes to safeguard the most vulnerable economic \ngroups. These interventions translated to increased \ngovernment expenditure, a decline in government revenue \n(as a result of lower demand for crude oil exports) and a \ngrowth in the government budget deficit and public debt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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