Evaluating and mitigating locally and nationally variable food security dynamics in Guatemala through participatory causal loop diagram building
Bibliographic record
Abstract
Abstract Various methods have been proposed to analyze national trends of malnutrition and food insecurity; however, these methods often fail to consider regional specificities that drive national food security dynamics. This case study seeks to close this gap through the novel use of participatory causal loop diagrams (CLDs) to analyze the malnutrition crisis and food security dynamics across diverse regions of Guatemala. Stakeholders from six municipalities with divergent food security outcomes, within territories of similar socioeconomic composition, created CLDs by identifying trends, causes, and consequences of malnutrition and food security. Characterizing and assessing these trends, referred to as the food security dynamic, are the primary goals of this paper. Key results include identification of the complex reinforcing relationship between marginalization, education, and health, which affects food insecurity and malnutrition in Guatemala in a nonlinear way. These results elucidate how similar communities can experience divergent food security outcomes and inform locally appropriate solutions. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".