The Relationship between Policy Design and Poverty Reduction: How the Design of Social Protection Programmes Address the Needs of the Poor in Ghana
Bibliographic record
Abstract
The implementation of social protection programmes has seen some significant success in poverty reduction among nations. This notwithstanding, there are some challenges in the designing of these programmes that sometimes defeat their intended purposes. For this reason, there is the need for a further consideration on the design of social protection programmes in reaching the poor. This paper assesses how the design of social protection programmes in Ghana takes into consideration the needs of the poor and other intended beneficiaries. The study adopts the concepts of social protection designs by Norton, et al (2001) and the beneficiary-targeting approaches by Rama and Dean (2016) to compare and assess how Ghana’s programmes are designed. This is purely a qualitative study that interviewed 20 respondents with adequate knowledge on the design of the social protection programmes. The study revealed that generally, in Ghana, the design processes of social protection programmes adopt more institutional-consultation approach than beneficiary/community-level consultation. On the part of selecting beneficiaries for social protection however, programmes like LEAP, School Feeding and the Capitation Grants were community based, that allow representatives of communities to select beneficiaries for the programme. The design of social protection programmes should be responsive to the needs of their intended beneficiaries, there is therefore, the need for broader consultations with the targeted beneficiaries. Consultations should, hence, not just be limited at the institutional levels.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".