Who participates in greening everyday urban space? Understanding community-led urban greening through the case of the Green Alleys of Montréal (Canada)
Bibliographic record
Abstract
• Understanding who participates in and who uses community-led greenspace can help explain spatial patterns of greenspaces. • Income is not the most prevalent variable, but social ties prove to be significantly important. • Homeowners and families with children are positively associated with participation and usage. • Parking-owners are negatively associated with participation and usage. • University degree holders and some professional profiles have higher participation. Cities have increasingly been implementing greening initiatives in conjunction with residents and local community organizations, yet little is known about the household characteristics of people involved in greening small-scale and ordinary spaces. In this study, we investigate involvement in and uses of the Green Alleys of Montréal. These greening programs, originating with and maintained by residents, are not determined by city planning but rather by volunteers. We conducted a survey of residents (N = 400) living adjacent to 66 Green Alleys in one borough of the city. Between 20 % and 29 % of respondents were involved in vegetation planting, meetings, and Green Alley committees in the past, and are currently involved in maintenance. Walking and cycling, talking with neighbours, playing with children, and driving along alleys to park cars are the most frequent uses of Green Alleys. Common variables that are significant in involvement and usage include having children, being homeowners, and having alley-based friendships. Income is not the most important variable but, advanced levels of education, being part of a visible minority and parking-space ownership were of greater significance. This is because Green Alleys require no financial outlay from residents, but instead draw upon their skill sets, their sense of social cohesion, and their interests in long-term benefits. The Green Alley Programs are hence determined by localized social factors and raise questions about green space connectivity and who may not join the programs. We call for customizing the programs to address these questions, making the programs accessible for more neighbourhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".