Policy Design, State Capacity and Management of Covid-19 Pandemic in Nigeria
Bibliographic record
Abstract
COVID-19 pandemic spread across the globe with alarming intensity. Due to its seemingly intractable nature, governments at various levels had to take safety measures aimed at containing the spread of the virus and cushioning its impact. In Nigeria however, lack of preparedness for emergency aggravated the debilitating effects of the deadly disease which have exposed the weak points of policy design, state capacity and institutional mechanisms. Though, federal government adopted mitigation measures through regulatory instruments to minimize the transmission of the virus, the policy responses are not commensurate with the magnitude of the problem, compared to what obtains elsewhere. There were no aggressive measures for early detection and diagnosis targeting individuals with symptoms. Many of the economic compensation packages that were approved to support and sustain people also encountered long administrative delays which are not ideal in an urgent situation as those in charge of the distribution of palliatives failed to grasp the depth of citizens’ deprivation, which requires swift remedial action. As a consequence, people became severely affected and had to pay the supreme price owing to leadership ineptitude. Based on this, the paper recommends well-crafted policy design and implementation; competent leadership; and provision of adequate health care.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".