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
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 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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