How Do We Belong Here? The Evolution and Expression of Incidental Spaces of Belonging for Toronto's Chinese Diaspora
Bibliographic record
Abstract
The Chinese diaspora of immigrant cities have historically created spaces of enclosed cultural spheres for collective survival and adaptation and this thesis examines those of Toronto and the Greater Toronto Area. Such spaces are often called “ethnic enclaves”, characterized by their homogenized demographic and corresponding services, spaces, and activities specific to those backgrounds. The subject of this thesis is an exploration of incidental spaces of belonging in these enclaves that were not explicitly built or programmed for building a sense of belonging but exist as such nonetheless because of what they contain. In a method of analysis analogous to the approaches taken by Interboro in The Arsenal of Exclusion and Inclusion and by Huda Tayob in her work in critical drawing, I examine the role of spaces, such as Chinese malls and plazas, private establishments, and streets of Chinatowns, and uncover how scales of belonging are developed through architecture, spatial planning, sign and language, and networks. \nAs transmigration and transnational economies proliferate due to globalization, the character of these cultural spaces of belonging have shifted since the first diaspora in the nineteenth century – strengthening the sense of belonging in some ways and eroding it in others. This has led to the rise of impermeable spaces, which import Chinese culture, alongside permeable spaces that export culture. As Sara Ahmed has argued, “it is the uncommon estrangement of migration itself that allows migrant subjects to remake what it is they might yet have in common”. This thesis explores these incidental spaces of belonging for Toronto’s Chinese diaspora and examines how physical, social, and temporal factors affect their permeability through field research, critical drawing, photography, and written analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".