NETWORKED GOVERNANCE IN AGRICULTURE-FOR-NUTRITION: A STAKEHOLDER MAPPING STUDY FROM GHANA
Bibliographic record
Abstract
Nutrition-sensitive agriculture approaches can improve farming household incomes, food security, and diet quality. Adopting nutrition-sensitive agriculture approaches means placing a nutrition lens on the policies, strategies, and investments in the food and agriculture sector without detracting from the sector's traditional goals of food supply. To understand the processes involved in developing agriculture-for-nutrition policies in Ghana, this paper examined the influence of stakeholders' interconnections using a visual participatory mapping technique, Virtual Net-Map. Three convening platforms were identified for stakeholder engagement: the Agriculture Sector Working Group, the National Agricultural Technical Committee, and the Public-Private Partnership Dialogue Platform. Sixty stakeholders with 188 connections were recognised for their involvement in agriculture-for-nutrition policymaking in Ghana. Fourteen stakeholders, twelve from government organizations and two from donor and development partner organizations, were identified as the most influential. International stakeholders (donors and development partners) were critical in funding agriculture-for-nutrition policymaking activities. While all stakeholders had a joint mandate to ensure policies were developed, the Ministry of Food and Agriculture led the policy development process in Ghana's food and agriculture sector. Moreover, government stakeholders notably received more support from other stakeholders for funding, advocacy, dissemination, and technical assistance than the support they offered. Generally, stakeholders were more engaged in technical assistance activities and least involved in disseminating agriculture nutrition information in the agriculture-for-nutrition policymaking process. The information on stakeholders' interconnections and influence showed areas that had the most and least stakeholder engagements, which will enable potential stakeholders to identify niche(s) to support the nutrition agenda in Ghana's food and agriculture sector and help Ghana meet the Global Nutrition Targets and the Sustainable Development Goals for 2025 and 2030, respectively. In addition, the evidence presented on Ghana's agriculture-for-nutrition policymaking network can lead to better ways of centralizing nutrition in agricultural policies and designing initiatives that encompass most, if not all, relevant stakeholders.
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".