Future Food System Research Priorities: A Sustainable Food Systems Perspective from Ontario, Canada
Bibliographic record
Abstract
Given the range and complexity of pressures on food systems across the globe, we suggest that future research on sustainable food systems can be clustered under three broad topics: the need for integration across multiple jurisdictions, sectors, and disciplines that includes different models of food systems and community visions of an integrated food system; the need for focus on tensions and compromises related to increased numbers and reach of sustainable food systems by scaling out and up; and the need for appropriate governance structures and institutions. Comparative research that works directly with community-based organizations to co-create and apply shared research tools and then engage in common assessment projects offers ways to develop more connected scholarship. More extensive work using concept maps, participatory action research, life-cycle analysis, and urban/rural metabolic flows may help to develop, animate, and answer future research questions in more integrated ways, and will build on opportunities emerging from more inclusive, connected, and multidisciplinary approaches. Work in Ontario helps to illustrate research exploring the three themes through embedded connections to communities of food in the ongoing research project Nourishing Communities.1 1 http://nourishingcommunities.ca
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".