3. Intervening with Agriculture: A Case Study of Guerrilla Gardening in Kingston, Ontario
Bibliographic record
Abstract
The purpose of this study was to analyze guerrilla gardening’s relationship to urban space and contemporary notions of sustainability. To achieve this two case studies of urban agriculture, one of guerrilla gardening and one of community gardening were developed. Through this comparison, guerrilla gardening was framed as a method of spatial intervention, drawing in notions of spatial justice and the right to the city as initially theorized by Henri Lefebvre. The guerrilla gardening case study focuses on Dig Kingston, a project started by the researcher in June of 2010, and the community gardening case study will use the Oak Street Garden, the longest standing community garden in Kingston. The community gardening case study used content analysis and semi-structured long format interviews with relevant actors. The guerrilla gardening case study consisted primarily of action based research as well as content analysis and semi-structured long format interviews. By contributing to the small, but growing, number of accounts and research on guerrilla gardening this study can be used as a starting point to look into other forms of spatial intervention and how they relate to urban space and social relations. Furthermore, through the discussion of guerrilla gardening in an academic manner more legitimacy and weight will be given to it as a method of urban agriculture and interventionist tactic. On a wider scale, perhaps it could even contribute to answering the question of how we (as a society) can transform our cities and reengage in urban space.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".