Using Land Inventories to Plan for Urban Agriculture: Experiences From Portland and Vancouver
Bibliographic record
Abstract
Problem: Urban agriculture has potential to make cities more socially and ecologically sustainable, but planners have not had effective policy levers to encourage this. Purpose: We aim to learn how to use land inventories to identify city land with the potential for urban agriculture in order to plan for more sustainable communities by answering two questions: Do land inventories enable integration of urban agriculture into planning and policymaking? Do land inventories advance both ecological and social dimensions of local sustainability agendas? Methods: We use case studies of two Pacific Northwest cities (Portland, Oregon, and Vancouver, British Columbia), comparing the municipal land inventories they undertook to identify public lands with potential for urban agriculture. We study how they were initiated and carried out, as well as their respective scopes, scales, and outcomes. Results and conclusions: We find that the Portland inventory both enabled integration of urban agriculture into planning and policymaking and advanced social and ecological sustainability. In Vancouver similar integration was achieved, but the smaller scope of the effort meant it did little for public involvement and social sustainability. Takeaway for practice: Other local governments considering the use of a land inventory should contemplate: (a) using the inventory process itself as a way to increase institutional awareness and political support for urban agriculture; (b) aligning urban agriculture with related sustainability goals; (c) ensuring public involvement by creating participatory mechanisms in the design and implementation of the inventory; (d) drawing on the expertise of institutional partners including universities. Research support: The Centre for Urban Health Initiatives at the University of Toronto provided financial support for writing up this research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".