Applying a climate-biodiversity-health framework to support integrated food systems planning and policy
Bibliographic record
Abstract
Food systems impact different aspects of sustainability and human life, such as pollution, health, climate change, biodiversity loss, water shortage, and soil infertility. However, in many places, food systems are neither resilient nor sustainable. Integrated planning approaches can help to overcome fragmented strategies and policies in ways that improve the sustainability and resilience of food systems. This study explores such an approach by applying a climate-biodiversity-health (CBH) nexus to local and regional food systems in the Comox Valley region, British Columbia, Canada. The CBH nexus is used as an analytical lens to identify systems relationships among food systems, climate change, biodiversity and health issues and strategies in the region. The employs a place-based approach entailing semi-structured interviews with provincial, regional, and local stakeholders in order to develop a holistic understanding of planning challenges, strategies and their outcomes through the CBH lens. Outcomes of this work include a system map that can be used as a framework for elucidating how various strategies align or conflict with different CBH imperatives and can be used to support integrated community sustainability planning and policy-making efforts. The framework is developed within the Comox Valley context, but it can be adapted to other communities. This paper details the development of this framework, the interconnections between different components, and how this framework can be adopted in other communities.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".