Building the field of food systems research: commentary on a research funder’s role
Bibliographic record
Abstract
BACKGROUND: The Food, Environment, and Health (FEH) program of the International Development Research Centre (IDRC) aims to improve the health of low- and middle-income country populations by generating evidence, innovations, and policies that reduce the health and economic burdens of preventable chronic and infectious diseases. A predominant focus of the FEH program is research related to consumer food environments that promote or enable healthy and sustainable shifts in consumption. An evaluation of the FEH program, led by the University of Toronto, provided an opportunity to analyse the approach and role of a development funder in building the field of food systems research. DISCUSSION: In this commentary, we provide an external evaluator's perspective on the IDRC's contributory role in building the field of food systems research, based on a secondary analysis of findings from a recent FEH program evaluation. We used the field-building framework outlined in Di Ruggiero et al. (Health Res Policy System, 2017) to highlight the strengths and challenges of the FEH's approach to field-building and determined that the program aligns with six of the seven features of the framework. The FEH program has enhanced support and awareness for food systems research, provided organized funding and capacity-building opportunities, multilevel activity to support research and its use, and strong scientific leadership, and set significant standards and exemplars. However, we also found that not all sociopolitical environments have fully recognized or valued food systems research and its use for policy change. CONCLUSION: The FEH program's field-building approach can be situated within the field-building framework, and it has been successful in laying the groundwork for building the field of food systems, particularly consumer food environments research. However, supportive external environments and further investments may be needed to achieve a critical mass of capacity, continue building communities of practice, and influence policy. The FEH program approach may serve as an exemplar and comparator for other research funding agencies looking to develop strategic research programming in the field of food systems research.
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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 | 0.230 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.071 | 0.073 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".