Towards a paradigm shift in social protection in developing countries? Analysing the emergence of the Ghana national unemployment insurance scheme from a multiple streams perspective
Bibliographic record
Abstract
The COVID 19 pandemic continues to cause a lot of uncertainty around the world. At the onset of the pandemic, governments responded with policies and programs to curb its devastating effects on citizens, and Ghana was no exception. Although the Ghanaian government introduced various stop-gap measures to mitigate the effects of the pandemic, the inadequacies of the extant social welfare system was badly exposed. Consequently, as the pandemic seethed on, there were calls for reform of the existing social protection system and the introduction of new programs, especially for those in the informal sector. In response, the government introduced a new National Unemployment Insurance Scheme (NUIS). How did this happen? What led the government to accept tentatively the need to reform and transform the social welfare system after years of policy padding and the dragging of feet? Drawing on Kingdon's Multiple Streams Framework, we argue that the pandemic created a policy window, which enabled policy enntrepreneurs to push the unemployment insurance idea to reform the existing social welfare system. The introduction of a NUIS, is seen as a paradigm shift in social protection and more broadly in social policy. The objective of this paper is to examine how the NUIS got on government's agenda, and whether the NUIS is a game changer in social protection in Ghana. We sourced information mainly from secondary sources.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".