Immigrants’ “Role Shift” for Sustainable Urban Communities: A Case Study of Toronto’s Multiethnic Community Farm
Bibliographic record
Abstract
As the ongoing health crisis has recently revealed, disparities and social exclusions experienced by immigrants in cities are now critical urban issues that can no longer be overlooked in the process of building sustainable urban communities. However, within the current practices aiming for social inclusion of immigrants, there has been an underlying assumption that immigrants are permanent “recipients” of their host society’s support, rather than potential “hosts” with abilities to support others in their society in the long-term. To question that assumption, this paper aims to identify immigrants’ degree of involvement by taking a multiethnic community farm in Toronto, Canada, as a case study to discuss the scope of the long-term inclusion of immigrants. Conducting a set of 15 life story interviews with participants of the Black Creek Community Farm (BCCF), the study identified what roles immigrants played within the group using the longitudinal analysis of individuals’ role-taking processes between 2010–2018. The paper identified three types of roles—recipient, assistant, and facilitator—taken by the participants during their involvement. The timeline of individual role types by year showed that more than half of the immigrants at the BCCF underwent a “role shift” to take an assistant and facilitator role that required higher engagement. The findings suggest immigrants’ orientations towards the BCCF have shifted from being the ones to be included to the ones including others in the local community over time, which confirms our hypothesis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".