Food System Sustainability across Scales: A Proposed Local-To-Global Approach to Community Planning and Assessment
Bibliographic record
Abstract
Interest in food systems sustainability is growing, but progress toward them is slow. This research focuses on three interrelated challenges that hinder progress. First, prevailing visions lack a concrete definition of sustainability. Second, global level conceptions fail to guide responses at the local level. Third, these deficiencies may lead to conflicting initiatives for addressing sustainable food systems at the community level that slow collective progress. The purpose of this article is to (1) describe the development of a framework for assessing food system sustainability which accommodates local-level measurement in the context of broader national and global scale measures; and (2) to propose a process that supports community determinacy over localized progress toward sustainable food systems. Using a modified Delphi Inquiry process, we engaged a diverse, global panel of experts in describing “success” with respect to sustainable food systems, today’s reality, and identifying key indicators for tracking progress towards success. They were asked to consider scale during the process in order to explore locally relevant themes. Data were analyzed using the Framework for Strategic Sustainable Development (FSSD) to facilitate a comprehensive and systematic exploration of key themes and indicators. Key results include a framework of indicator themes that are anchored in a concrete definition of sustainability, stable at national and global scales while remaining flexible at the local scale to accommodate contextual needs. We also propose a process for facilitating community-level planning for food system sustainability that utilizes this indicator framework. The proposed process is based on insights from the research results, as well as from previous research and experience applying the FSSD at a community level; it bears promise for future work to support communities to determine their own pathways, while contributing to a more coordinated whole.
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How this classification was reachedexpand
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".