The Socio-Environmental Impacts of Public Urban Orchards: A Montreal Case-Study
Bibliographic record
Abstract
The rapidly increasing urbanization of the world creates important environmental and social problems. By bringing the cultivation of food closer to where people live, urban agriculture could contribute to alleviating some of these, especially when involving the participation of the residents. Here the potential socio-environmental impacts of public urban orchards was studied, using as a case-study a public urban orchard planted in 2010 in Sainte-Anne-de-Bellevue on the island of Montreal (Quebec, Canada) by the city administration. The socio-environmental constructs evaluated were: place attachment, social capital, food and food system knowledge, and environmental knowledge. Observations of the users of the site were performed, and semi-directed interviews were conducted with eleven users of the bike path and two members of the city administration who have developed the project. The interviews with the users were analyzed using a mixed inductive and deductive qualitative approach. Evidence of positive impacts was found for place attachment, social capital, and food knowledge, while no evidence of impacts was found for food system and environmental knowledge. Impacts on social capital were seen for most of the social capital components studied, but not for bridging social capital. Impacts on place attachment appeared to take place in large part through an increased appreciation of the city administration, thereby also possibly increasing the trust in the administration (social capital). However, this effect appeared to be dependent on a level of maintenance of and communication about the orchard project perceived as adequate by the residents. Finally, the interviewees manifested a high level of interest in participating in maintenance or harvesting activities around the orchard, mainly for interaction with their community. Based on the results I suggest that implementing participatory activities and providing more information about the orchard, the food system and the environment could increase the impacts on the four constructs studied, and I propose other potential means through which urban agriculture could impact socio-environmental sustainability, namely through improving quality of life and reducing urban sprawl. Though further research is needed to evaluate the extent to which the results are transferable to other contexts, this study should be of interest to city administrations seeking cost efficient means of positively contributing to socio-environmental sustainability and to the individual wellbeing 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".