Assessing Community Contributions to Sustainable Food Systems: Dietitians Leverage Practice, Process and Paradigms
Bibliographic record
Abstract
Abstract Food systems are not sustainable, and efforts to address this are paralyzed by the complex networks of food system actors and factors that interact across sectoral and geographic scales. Actions at the community level can positively contribute toward globally sustainable food systems (SFS). Assessing such contributions has two central challenges: 1) a lack of methods that support alignment between communities and across scales, balanced against the need to involve the community in developing relevant indicators; and 2) the absence of adequate, fine grained data relevant to the community. Addressing these two challenges, this paper illustrates a proposed procedure that supports community engagement with, and assessment of, their contributions. Engaged by a community of Canadian dietitians, researchers used the Delphi Inquiry method, guided by the Framework for Strategic Sustainable Development, to address the first challenge, and causal loop diagrams informed by the Cultural Adaptation Template to address the second. Indicators were developed for dietitian-identified actions and outcomes for SFS. Modeling indicator interactions provide insight into how some actions are influenced by and reinforce the value placed on SFS within the professional cultural paradigm, as well as priority areas for action and measurement. Process-oriented assessment is useful in the context of partial and subjective understandings of a dynamic system, and supports continual adjustment in action. This article offers theoretical and practical insight for community engagement in addressing some of the systemic challenges in food systems. It accommodates community-based knowledge, applies process-indicators, and emphasizes the importance of cultural paradigms as a driving force of community-level actions, and overall system change. Under current conditions, facilitating SFS literacy among dietitians can amplify adaptations for broader SFS development.
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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 | 0.048 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".