Transnational actors and policymaking in Ghana: The case of the Livelihood Empowerment Against Poverty
Bibliographic record
Abstract
Transnational actors (TNAs) are a part of the global social policy process. But questions of their roles and involvement in the process remain unanswered. Using a qualitative research to study Ghana’s adoption of the Livelihood Empowerment Against Poverty (LEAP), this article brings new evidence to light on how TNAs influence social policies in developing countries. Contrary to arguments that stress imposition as the main policy diffusion mechanism, it is shown here that TNAs combine multiple strategies including ideational, institutional, and material incentives to influence social policies in particular countries. As idea purveyors at the transnational level, TNAs are linked to the national policy process through their connections with policymakers and, more specifically, through policy discussions at regular sector working group meetings. From this perspective, ideas are shared and availability of support toward policy development is communicated.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".