An Approach for Integrating and Analyzing Sustainability in Food-Based Dietary Guidelines
Bibliographic record
Abstract
International organizations, governments, researchers, and activists have proposed the need for deeper integration of sustainability considerations in national food-based dietary guidelines (FBDGs). Yet, as recent scholarship advances the conversation, questions remain around how to effectively frame and address the interconnectedness of multiple sustainability domains. Little systematic analysis has evaluated how current FBDGs have integrated complex messages about socially, environmentally, and economically sustainable consumption practices with nutrition and health messages. This study had two nested objectives: (i) to examine the validity of an existing sustainable diets framework by assessing how sustainability concepts have been framed and included in national FBDGs available from 2011 to 2019 and (ii) to describe a novel analysis approach that augments an existing framework which integrates sustainability domains and can be adapted for use by future FBDGs. A qualitative content analysis was used to examine sustainability concepts found in 12 FBDGs and supporting documents available in English that were developed for use in 16 countries across Europe, North and South America, and Asia as of 2019—from a global review of those published prior to 2016 and gray literature review of publications between 2016 and 2019. Health domains were the primary frame found across the FBDGs examined, but documents also commonly incorporated agricultural, sociocultural, and economic sustainability principles. Analyzed documents were used to adapt an existing policy analysis framework into a “Sustainability in FBDGs Framework.” This proposed framework contributes a novel analysis approach and has five core domains that are interconnected: health and nutrition, food security and agriculture, markets and value chains, sociocultural and political, and environment and ecosystems. This study adds to the growing body of literature related to sustainable food systems and dietary guidelines by presenting how sustainability framing in FBDGs can be used to further develop a comprehensive framework for integrating sustainability domains. While this project helps to validate previous work, further analyses of FBDGs which have emerged since this study and those not available in English are needed to improve the guidance approach described here and for assessing the incorporation of sustainability domains in future FBDGs. This work is useful in informing processes for policy developers to integrate sustainability considerations into their national FBDGs.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".