Scenario analysis using community insights for improving local food system planning: Application of a climate-biodiversity-health framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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