Cultivating the city: An inquiry into the socio-spatial production of local food
Bibliographic record
Abstract
Once considered out of place in cities, urban agriculture is an increasingly common practice. This dissertation considers questions of urban agriculture and local food through a “production of space” lens. This framing allows for an expanded empiricism, opening up the investigation of urban agriculture to include a consideration of spatial practices, lived experiences, and varied representations. In addition to theorizing urban agriculture through a production of space lens, this dissertation draws on multiple qualitative methods, including interviews, participant observations, and self-ethnography, to develop and contribute to a socio-spatial mapping of local food space in Edmonton. Through these methods, this dissertation contributes to a better understanding of the complex processes and diverse meanings involved in the production of urban agriculture space. Rather than focusing on a singular site of urban agriculture, I consider its production at various scales, from urban farm to city-region, examining the particulars of each case and the relationships between them through theoretical discussion. The dissertation concludes by introducing the concept of the urban agriculture imaginary, emphasising the ways in which urban agriculture exists as a symbolic landscape – a set of widely circulated representations and ideas about the practice that recasts the city in different ways.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".