<scp>GOVERNING URBAN AGRICULTURE</scp>: Formalization, Resistance and Re‐visioning in Two ‘Green’ Cities
Bibliographic record
Abstract
Abstract As municipalities across the global North highlight urban agriculture as a marker of their ‘greenness’, how can we best understand how the spaces and practices of urban food production are governed? This article develops an analysis of urban agriculture as a complex site of governance in which numerous interests engage. We underscore the politics of governance, through which some actors resist the imposition of a narrowly normative and exclusive notion of urban agriculture and against which they envision and enact alternatives. The article contributes to efforts to transcend the often dichotomous framing of urban agriculture as radical or neoliberal, formal or informal, political or post‐political by employing ‘everyday governance’ and ‘everyday resistance’ as lenses through which to focus on the prosaic practices of engaging with, pushing back against, and stepping beyond the imposition of hegemonic models of urban agriculture. We argue that the co‐constitutive, ‘braided’ nature of urban agricultural governance is revealed through attention to the manifold forms of negotiation and resistance to formal urban agricultural governance. Moreover, our perspective highlights the ways that some practitioners are excluded by, challenge, or re‐vision formal definitions of urban agriculture. We draw on the cases of Portland, OR and Vancouver, BC to illustrate our argument.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.070 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".