Spaces, Systems and Infrastructures: From Founding Visions to Emerging Approaches for the Productive Urban Landscape
Bibliographic record
Abstract
The proliferation of urban agriculture on an array of urban spaces is one of the more visible responses to perceived failures of contemporary food systems. This paper seeks to identify fundamental strategies connected to food system change efforts, linking these with diverse attempts at designing and planning the productive city. It first situates the contemporary concept of the productive city within a broader historical dialogue of foundational figures in urban and regional planning, architecture, and landscape architecture for whom food production was a central component of future cities. Recently, a growing number of practitioners have theorized the need for integrating urban agriculture in urban design and planning. Across this spectrum of emerging theory and practice, we identify three approaches to designing productive cities. First, spatial design strategies identify new territories for food production. These offer the potential for systems design thinking that links the individual spaces of production to other sectors of food systems that extend across networks of spaces and multiple scales. Finally, both spatial and systems design involve strategies of designing productive infrastructures of soils, water, nutrients, and other essential flows. The engagement with spaces of production, food systems, and productive infrastructure opens up a range of challenges as well as opportunities for emerging forms of practice and design thinking for the productive city.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.103 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".