Collaborative governance in social protection programs in developing countries: evidence from Ghana
Bibliographic record
Abstract
Abstract Social protection has increasingly become a central tool of governments to deal with vulnerabilities and poverty in society. In the context of policy and governance challenges, there has been a shift from a governance approach that focused extensively on governmental actors to one involving a multiplicity of actors in a process of collaborative governance. However, limited studies have been conducted in terms of how stakeholders have been collaborating with the government to enhance the effectiveness of policy and program development and implementation. Focusing on Ghana’s Livelihood Empowerment Against Poverty (LEAP), one of Ghana’s flagship social protection programs, we present the collaborative implementation architecture that underpins the program’s implementation. Using data obtained mainly from secondary sources complemented with key informant interviews, within the framework of collaborative governance, we argue that the LEAP’s implementation architecture is a good example of collaborative governance and may account for its popularity. This article observes that the design and management of the LEAP program is shaped by vertical collaboration involving engagements between transnational actors and state officials observed at the deliberative and design stages, while the implementation and delivery of the program rely on horizontal collaboration that prioritizes the active involvement of beneficiary communities.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".