You can have your park and eat it, too: designing a public food forest for a Winnipeg park
Bibliographic record
Abstract
The industrialization and commodification of the food system has brought about unintended environmental, social, and psychological consequences: ecosystems have been degraded, food-related traditions and social ties have been lost, and food illiteracy is on the rise as urbanized society becomes increasingly distanced from the processes of food production. Urban agriculture can address these concerns by providing opportunities to connect communities, promote food literacy, and create a demand for more sustainable food production. Introducing urban agriculture to public parks could diversify park programming and increase public engagement with food production. However, parks can be challenging urban agriculture sites due to their aesthetic standards and open access to the public. Public food forests respond well to these challenges, making them especially well suited to public parks compared to other forms of urban agriculture. Successful implementation of public food forests in parks requires cooperation and partnerships between professional designers, local communities, and government agencies, as well as positive relationships within the food forest community. This practicum demonstrates these ideas through the design of a public food forest in a Winnipeg park, aiming to inspire designers, government bodies, and communities in Winnipeg and beyond to reconceptualize public parks as potential places for sustainable, aesthetically mindful, and socially beneficial food production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".