Enhancing urban nature and place-making in social housing through community gardening
Bibliographic record
Abstract
There is a growing body of literature examining the multifaceted benefits of community gardens for environmental and social wellbeing. While there are studies examining the increase of grassroots urban gardening initiatives in low income and vulnerable communities, there remains a need for research that explores the lived experiences of individuals in social housing communities with urban nature and community gardens. Individuals living in urban social housing may experience inequalities in access to green infrastructure both within their housing estates as well as the surrounding community. For the past two decades, the Community Greening program at the Royal Botanic Garden Sydney, Australia, has implemented outreach initiatives to support the development and maintenance of community gardens in social housing communities in New South Wales. This article presents the findings from a study conducted with participants at six new community gardens built in social housing communities, focusing specifically on focus group interviews with residents and staff questionnaires to examine the perceived impact of the Community Greening program. The participants’ narratives highlight program outcomes across the key themes of community gardens as sites for knowledge generation and connection to nature, sense of community, and improving residents' sense of pride and public perceptions of public housing. The findings suggest that community gardens enhance green infrastructure in social housing estates and their broader urban locales by improving access to green space and promoting place-making in social housing contexts. We conclude with a discussion of the recommendations and lessons learned that may help to inform future policies and practices when setting priorities that promote social and environmental sustainability in social housing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".