Scenario analysis using community insights for improving local food system planning: Application of a climate-biodiversity-health framework
Bibliographic record
Abstract
Addressing the complexities of local food systems planning requires integrating community insights to design policies that meet stakeholder expectations and guide targeted interventions. This study employs systems to analyze local food systems planning within a Climate-Biodiversity-Health framework. By gathering stakeholder input and community perspectives, it aims to identify critical leverage points within the complex network of interconnected challenges affecting food systems. Using a survey designed around the connections of a systems map, 138 responses were gathered, and 15 nodes functioning as leverage points were identified across various domains, including climate, biodiversity, food, and governance. Mental Modeler software was used for a ‘what-if’ scenario analysis to explore the potential implications of the identified leverage points on overall food systems concerning climate, biodiversity, and health factors. This research contributes methodological and empirical insights to the literature by experimenting with a systems-based approach for comparing perspectives of practitioners and broader community members on food systems issues and strategies. The research revealed both areas of alignment and divergence that highlight the need for planning approaches that are effective and publicly trusted. The study identifies a mix of agro-ecological and governance interventions for building a resilient food system that supports climate action, biodiversity, and community well-being. Furthermore, the study aims to showcase the practical application of community knowledge in system analysis and intervention identification, contributing to the advancement of sustainable and resilient food systems.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".