The socio-environmental impacts of public urban fruit trees: A Montreal case-study
Bibliographic record
Abstract
In the past two decades, worldwide interest in urban agriculture has rapidly increased amongst residents, city administrations, businesses and researchers, and a diversity of social and environmental benefits were found or argued for the practice. However, most studies have been conducted on commercial activities or on community-gardens, which are either private or of restricted access, and to our knowledge no study is available yet on the impacts of public produce, that is, food grown in public spaces and freely accessible to passersby. Yet, because its access is unrestricted, public produce might impact the community in a different and perhaps more widespread fashion than community gardens. To begin to address this gap, we studied potential socio-environmental impacts of public urban fruit trees, focusing on social capital, place attachment, food and environmental knowledge, using a public urban orchard located in Montreal, Quebec as a case-study. Semi-structured interviews were conducted with users of the site and analyzed using a mixed inductive and deductive qualitative approach. Evidence of positive impacts was found for social capital (including the relationship with the city administration), place attachment, and food knowledge, while no evidence was found for environmental knowledge. The results also strongly suggest that implementing participatory activities and providing more information about the orchard, the food system, and the environment on the site could increase the impacts on the four social phenomena studied. This study suggests that public, unrestricted-access urban agriculture could have diverse and direct socio-environmental impacts. The findings should be of interest to city administrations seeking cost-efficient means of positively contributing to socio-environmental sustainability and to the well-being of their residents, as well as to researchers interested in the relationship between urban planning and socio-environmental sustainability.
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 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".