Walking through the gardens: A case study of Iranian community gardeners in three urban community gardens, in Montreal, Canada
Bibliographic record
Abstract
This thesis examines the place-making strategies employed by some Iranian immigrants in three urban community gardens in Montreal, Canada. I argue the practices of place-making within the garden is a complex and multilayered process in which heterogeneous actors (human and non-human) play central roles. Prosaic pleasures invoked through enchanting encounters and childhood memories of a beloved garden motivate some people to spend their time and energy within the garden in the hope of re-enchantment. In this theoretical synthesis, I examine how, through inhabiting the garden, these gardeners develop a sense of belonging and attachment to Canada as their new home. I demonstrate that place-making within a community garden goes beyond altering the physical landscape of the garden. Through the process of constructing an alternative home within the community garden, immigrants form family-like relationships, improve their health and well-being, and also cultivate a sense of stability and belonging. This thesis examines the entangled relationships between humans and non-humans within the community gardens. I propose that place-making within the community garden is not just a human achievement, but rather it is co-constructed by heterogeneous actors. It is notable that this thesis acknowledges that a human is the most powerful actant in the process of place-making. However, it also highlights the roles of non-humans as it would be a huge omission if we did not credit the active positions of non-humans in our lives.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".