Designing for Food and Agriculture: Recent Explorations at Ryerson University
Bibliographic record
Abstract
Strategies to enable alternative urban food systems cannot be developed alone by those involved with the production and distribution aspects of food systems. It is important for architects, landscape designers and planners to be part of the process of conceiving and implementing innovative food-system thinking. Environmentally focused building standards and models for sustainable communities can easily incorporate farmers' markets, greenhouses, edible landscapes, permeable paving, green roofs, community gardens, and permaculture and other food-related strategies that complement energy generation and conservation, green roofs, living walls, and other approaches that have been more commonly part of sustainable built-environment initiatives. Recently, architecture faculty and students at Ryerson University in Toronto and at a number of other universities have been exploring the intersection of these disciplines and interests. This paper will show how Ryerson tackled agricultural and food issues as design challenges in projects that included first-year community investigations, student-run design competitions, third-year studio projects and complex final-year thesis projects. These projects that dealt with food issues proved to be excellent entry points for addressing a range of design challenges including social inclusion, cultural context, community design and sustainable building practices.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".