Urban gardening in public space: Policy approaches in Greater Sydney
Bibliographic record
Abstract
Citizen-led initiatives, like urban gardening, are increasingly transforming urban public spaces. Such practices of DIY urbanism have prompted varied policy responses from local governments across the world. Public urban gardening is a form of citizen initiative that involves the small-scale appropriation of publicly accessible open space for gardening with or without the approval of the landowner, usually the local authority. Growing plants on public footpaths and in nature strips can range from unregulated guerilla gardening to publicly authorised community gardens. These novel forms of shared responsibility and stewardship of public space can challenge traditional forms of urban governance, which depend on a separation between public and private. At the same time, neoliberal forms of urban governance that have developed in the context of austerity have encouraged citizen empowerment and responsibility for the management of public space in cities, with varying outcomes. This article investigates the range of policy approaches to public urban gardening in Greater Sydney, Australia, a metropolitan area comprising 33 local governments. We employ Vedung’s typology of public policy instruments for the qualitative analysis of policy documents and identify five types of policy approaches within Greater Sydney. We find that trust-based policy instruments (being notified of an activity) are increasingly preferred by local government and enable a collaborative approach to the governance of public urban gardening. The case illustrates a diversity of public policy instruments, depending on the local context, for incorporating public urban gardening into policy and potentially contributing to a healthy and sustainable city.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".