Bibliographic record
Abstract
Urban food trees (UFTs) are increasingly present in cities and the urban forest through the establishment of community orchards, food forests, and edible landscaping. UFT sites are novel within Canadian cities and are planted for their multiple benefits such as food production, biodiversity, and aesthetics. Although beneficial to communities, little research has been conducted on the relevant policies, broader governance approach and their integration into urban forestry practice. Furthermore, as sites open to the public there is opportunity for urban populations to harvest and engage with UFTs, but little is known about the everyday accessibility of the sites, and ways that these novel sites develop human-nonhuman relationships. Therefore, this dissertation seeks to fill these gaps by exploring the governance, accessibility, and more-than-human relationships at multiple UFT sites in cities across Canada. To answer these research objectives semi-structured interviews were conducted with municipal actors, intercept and go-along walking interviews were conducted with site coordinators, volunteers, and foragers, and participant observation was conducted at individual sites in Calgary, Edmonton, Toronto, and Victoria. All the interviews were transcribed and coded in NVivo 12. The results indicate that UFTs are still marginal within the practice of urban forestry through the lack of resources, direct policies, and discourse primarily focused on potential risks over benefits, with some opportunities for co-governance of UFT sites. Furthermore, the results indicate that proximity and distribution of UFTs alone is not sufficiently accessible, with other considerations such as knowledge of and engagement with the decision-making process, as well as socio-economic factors. Finally, UFT sites provide an opportunity for urban populations to encounter and develop varied relationships with nonhumans, leading to opportunities for demonstrating care and learning. This dissertation provides an early look into UFT governance and on the ground practices at UFT sites. These findings are important because they can be applied to current and future iterations of UFT sites for further integration into current urban forestry practice and inform the development of more accessible and equitable sites for urban residents, both human and nonhuman. Further research is required to scale up the findings and understand Indigenous perspectives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.035 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".