A systems thinking approach to examine local food systems planning through a climate-biodiversity-health lens: A Comox Valley case study
Bibliographic record
Abstract
Food systems are highly vulnerable to the effects of anthropogenic climate change and environmental degradation. At the same time, food systems contribute significantly to the production of greenhouse gases that negatively impact ecosystems. Such a vicious cycle of cause and effect demands a transition to sustainable food systems, and this is best done through integrated planning and policy perspectives that tackle interconnected socioeconomic and environmental concerns and goals. This research applies systems thinking to map relationships among food systems planning and other sustainability priorities, namely those related to climate, biodiversity, and health. The study engaged stakeholders in the Comox Valley region, British Columbia, to develop a causal loop diagram, which was subsequently analyzed using the Girvan-Newman community detection algorithm to identify closely connected nodes or 'clusters'. The results of this work provide a comprehensive understanding of how local food systems' challenges and opportunities connect and integrate with other local and regional sustainability objectives. The research identified 123 systems nodes, which were organized into five categories: food, climate, biodiversity, health, and governance. The community detection method was applied to reveal 15 clusters among these nodes. The methodology employed in this research, integrating the development of a causal loop diagram and applying community detection, is novel and contributes to the growing body of literature advocating for an integrated planning approach to address the complex challenges facing local and regional food systems. • The Climate-Biodiversity-Health nexus is integral to food system planning • An integrated planning process involves stakeholders from various sectors • Systems approach reveals links between socio-environmental and governance components • Systems approach finds interventions and fosters collaboration among stakeholders • Community detection uncovers system structure and aids decision-making
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".