Food security outcomes in agricultural systems models: Current status and recommended improvements
Why this work is in the frame
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Bibliographic record
Abstract
Improvement of food security is a common objective for many agricultural systems analyses, but how food security has been conceptualized and evaluated within agricultural systems has not been systematically evaluated. We reviewed the literature on agricultural systems analyses of food security at the household- and regional-levels, finding that the primary focus is on only one dimension of food security—agricultural output as a proxy for food availability. Given that food security comprises availability, access, utilization and stability dimensions, improved practice would involve more effort to incorporate food access and stability indicators into agricultural systems models. The empirical evidence base for including food access indicators and their determinants within agricultural systems models requires further development through appropriate short and long-term investments in data collection and analysis. Assessment of the stability dimension of food security (through time) is also particularly under-represented in previous work and requires the development and application of appropriate dynamic models of agricultural systems that include food security indicators, coupled with more formalized treatment of robustness and adaptability at both the regional and household levels. We find that agricultural systems models often conflate analysis of food security covariates that have the potential to improve food security (like agricultural yields) with an assessment of food security itself. Agricultural systems modelers should exercise greater caution in referring to analyses of agricultural output and food availability as representing food security more generally.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it