Identifying indicator needs for food system transformation: South and Southeast Asia
Bibliographic record
Abstract
Clear measures of progress on food system transformation can provide decision-makers with the visibility to course-correct to realise desired impacts and can help ensure accountability.To this end, there is a need to develop, test, and validate novel methods and metrics for assessing food systems transformation.To ensure that such work is grounded in local food system stakeholders' needs, GAIN consulted national stakeholders across four Asian countries (Bangladesh, India, Indonesia, and Pakistan) to identify priority indicator gaps for monitoring food systems transformation.These consultations drew from an analysis of each country's food system transformation pathway, existing indicators, and the results from similar stakeholder workshops in Africa.National stakeholder workshops were held with diverse participants in three of the countries, while stakeholder interviews were used in India.Across all countries, some similar themes emerged, such as sustainable and climate-smart agriculture, small and medium-sized enterprises, food safety and quality, consumption behaviour, policy alignment, and food system governance.There was a strong focus on policy actions, sustainability, and resilience as crosscutting themes.Women and youth were mentioned as groups requiring particular attention in metrics development, including the wage disparities between men and women, inclusion of women and youth in decision-making process, and youth access to finance and agri-business.The results from the workshops will be used to inform GAIN's future work in developing metrics and methods to understand and help countries track their food systems transformation. KEY MESSAGES• Understanding and promoting food system transformation requires appropriate metrics and methods, some of which may not yet exist.• To understand national stakeholder needs for food related methods and metrics, GAIN conducted stakeholder consultation workshops and high-level interviews in four countries in Asia in 2024, complementing similar work in five African countries in 2023.• Consulted stakeholders identified priority themes for new metric development, which are summarised in this paper.• Stakeholders in India described a markedly different approach to food systems transformation and measurement compared to the other three countries but identified similar priority areas for measurement, including unique agrarian economy and nutrition security needs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".