Towards a Climate-Smart Food System: A Theory of Change and Impact Metrics to Trigger Farming and Societal Change
Bibliographic record
Abstract
There is significant interest in sustainable food production practices in Canada and worldwide due to the challenges caused by the Russia-Ukraine war, land degradation, and climate change. Sustainable food production is a food system that provides affordable, nutritious food while preserving and restoring natural resources and generating robust ecosystem services such as carbon sequestration, water filtration, and retention. This report explores multiple routes to foster improved social, ecological, and economic impacts associated with alternative practices promoting sustainable food production. It identifies core problems that prevent agricultural systems and their food chains from implementing (more) sustainable practices. The report mobilizes a Theory of Change (TOC) to outline possible interventions and metrics to implement (community-based) interventions to promote shared principles of sustainable production and create communities of practice. The TOC was developed in consultation with a set of actors in the food chain (including farmers, financial institutions, municipal governments, food processors, NGOs and industry associations) during a nine-month research intervention in Canada (2023), complemented by a literature review. Thanks to this co-creation process, the proposed interventions and metrics to measure and track improvements at the farm and societal levels presented in this report are outcomes-based and bottom-up. This enables agricultural communities and actors in the food chain to pursue alternative routes to improve outcomes. The report also discusses incentives to pursue sustainable food production, either explicit (e.g. monetary payments, contractual clauses) or implicit (e.g. social norms, cultural values, network-based engagement of food chain actors). Lastly, it outlines a potential research design to test the suggested interventions, metrics, and incentives in a Randomized Control Trial (RCT).
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".