The common ground: informal growing and stewardship on public land in Toronto, and the potential for agreements that benefit all
Bibliographic record
Abstract
While the environmental, social, and health benefits (Middle et al., 2014; Soga et al., 2017; Twiss et al., 2003) of community-led growing and stewardship activities (e.g. gardening, planting native species, invasive species management) are recognized by planners, the presence of people who grow on or steward public land informally (illegally) indicates that needs are not being met by existing programs. This research investigates how the goals of informal growers/stewards and public landowners align, and what barriers would need to be overcome to form mutually beneficial agreements that leverage the passion and interest of these action-oriented citizens. Based on twelve interviews with people involved in informal growing or stewardship activity in the City of Toronto (either as growers/stewards, public landowners, or other professionals), three cases of existing, potential, and emerging agreements were studied for how they might meet the needs of each party and create benefits for all. Key words: stewardship; guerilla gardening; public land; citizen participation; green infrastructure; City of Toronto
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".