Growing pains: Successes and barriers in London, Ontario’s urban agriculture strategy
Bibliographic record
Abstract
Urban agriculture (UA) is gaining momentum across Canada in light of the COVID-19 pandemic, with growing public interest and municipal responses such as the City of London, Ontario’s 2017 London Urban Agriculture Strategy (LUAS). This paper examines the implementation and impact of the LUAS, drawing on interviews and workshop insights from for-profit and nonprofit urban food producers, processors, and distributors. Building on a prior study by Miedema (2019) of the city’s Hamilton Road neighborhood, we analyze the strengths, weaknesses, and challenges of new and existing UA initiatives across the city. Three factors emerged as critical to UA’s success: municipal governance matters, community efficacy, and the rising cost of living compounded in a post-pandemic context. We assess how London’s strategy has enabled progress—such as bylaw amendments—but also where it falls short due to limited communication, persistent land access issues, jurisdictional misalignments, and a lack of proactive leadership. Our findings contribute new insight into the institutional barriers facing UA in midsized cities and identify three key knowledge and capacity gaps—leadership, technical guidance, and communication—that must be addressed to support sustained UA implementation. We offer recommendations for closing these gaps through coordinated efforts across municipal, private, and community sectors. Ultimately, this research advances the conversation on how cities can more effectively support inclusive, resilient, and culturally valued urban food systems rooted within a food justice framework.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".