Urban Food Systems Strategies: A Promising Tool for Implementing the SDGs in Practice †
Bibliographic record
Abstract
The UN’s 2030 Agenda for Sustainable Development, and the transition from Millennium Development Goals (MDGs) to Sustainable Development Goals (SDGs), heralds an important turn in global sustainability policy. With implementation now taking place in all countries, regardless of GDP, a key question is how affluent governments in large metropolitan areas can effectively contribute to global sustainable development. This paper argues that urban food systems strategies—a relatively new tool in local policymaking in the Global North—have the potential to amplify and consolidate national and international efforts in this direction and facilitate a more synergistic approach to SDG implementation. An in-depth comparative analysis of the 2030 Agenda and the sustainable food systems strategies of five of the ten largest cities in North America—New York, Philadelphia, Los Angeles, Chicago, and Toronto—helps to uncover key gaps and areas of convergence between goals, objectives, and evaluation frameworks. Goal- and indicator-level analyses cast light on promising areas for cross-jurisdictional cooperation and suggest that, while not without limitations, urban food systems strategies offer manifold pathways to streamline global, national, and local implementation efforts and effectively forward the 2030 Agenda over the next decade.
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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 | 0.041 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".