“Provide our basic needs or we go out”: the COVID-19 pandemic lockdown, inequality, and social policy in Ghana
Bibliographic record
Abstract
Abstract The effects of the coronavirus disease (COVID-19) pandemic cuts across every facet of a nation’s life. The near collapse of economies with the attendant job losses has brought forth the need for effective social policies, particularly in developing countries, that can serve citizens in dire need. Consequently, many of these countries have had to craft emergency social policies to help their citizens. Ghana is no exception. While measures to control the spread of the pandemic, such as lockdowns and restrictions on movement and gathering, were timely, they negatively impacted the poor, most of whom work in the informal sector and depend on daily survival activities such as buying and selling basic goods. As a result, some of the measures were ignored as people feared they would die from hunger rather than from the pandemic. Thus, governmental response to the pandemic was highlighted by policy layering and exposed the fragile social support systems in existence. The challenges of responding adequately to the pandemic underscore the importance of a transformative social welfare regime in ensuring the protection of citizens. This paper, based on desk research, explores the limitations of the existing social policy framework, which became manifest during the implementation of Ghana’s pandemic policies. Policy layering by government continues to weaken Ghana’s social welfare system, and this affected the official response with respect to the social issues that have emerged due to the pandemic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".