Vision, relationships, patience … and power: A qualitative analysis of how policy agents scale up cross-sectoral policy for nutrition
Bibliographic record
Abstract
Malnutrition in all its forms presents an urgent global health challenge and food systems transformation is a critical component of the necessary policy response. However, cross-sectoral policy engagement between nutrition policy makers and the policy sectors responsible for food systems change has proved challenging. This policy analysis focused on how policy makers act as agents within institutions to advance cross-sectoral policy making on food systems and nutrition, informed by theories of policy making and power. Forty-three interviews were conducted with policy actors working at global, regional and national level, in relevant sectors. The interview data were analysed iteratively, informed by theory, with a focus on actor roles, characteristics, skills and capacities and the dynamics between these dimensions. We found that successful cross-sectoral policy engagement for nutrition resulted from a dynamic interaction between agents and institutional structures, within which policy agents were able to exert 'power to' influence outcomes, and exert 'power with' multidisciplinary teams and cross-sectoral colleagues to effect change. These policy actors were able to shape ideas and create cultures and norms that supported cross-sectoral engagement, which led to effective engagement between policy sectors, characterised by constructive dialogue, shared decisions, trust, goodwill and balancing objectives across sectors. The result of this engagement was consideration of nutrition in policy making in sectors other than health, in ways that resulted in tangible policy outcomes. Success in cross-sectoral policy engagement was seen as strengthening both institutional support for cross-sectoral action on nutrition, and the ability of policy actors to overcome barriers.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".