Is Urban Agriculture a Game Changer or Window Dressing? A Critical Analysis of Its Potential to Disrupt Conventional Agri-food Systems
Bibliographic record
Abstract
Is urban agriculture capable of becoming a ‘game changer’, contributing to the sustainable transition of our conventional agri-food systems? Or is it more likely to be ‘window dressing’, characterized by limited participation and influence? The answer depends upon how we measure system change. The value of urban agriculture is often measured in physical – caloric – terms. By assessing the multiple emergent effects of urban agriculture activities through an extensive, in-terdisciplinary literature review, this article provides a more informed context to a discussion of the disruptive potential of urban agriculture. Several features of urban agriculture suggest its potential to be an important contributor to agri-food system transition; however, a number of key challenges must be acknowledged and addressed. Ultimately, producing food in cities is not inherently transformative in and of itself, but the potential and observed new forms of social en-gagement emerging in many contexts create institutional conditions that can disrupt conventional agri-food systems by building social capital as much as physical capital.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".