Sino-Vietnamese: Chinese sub-ethnic relations in Toronto’s Chinatown West District Paper presented at the “Conference on Subethnicity in the Chinese Diaspora,”
Bibliographic record
Abstract
an increase in Sino-Vietnamese businesses moving into the area over the years since the late 1980’s. Other Chinatowns have developed in the surrounding suburbs, such as around Broadview, Richmond Hill and Scarborough with concentrations of Hong Kong, Taiwanese or Mainland Chinese populations. This paper will explore the relationship between Sino-Vietnamese and Chinese in a Chinatown context. Four Sino-Vietnamese businesses located in Vietnamese dominated mini-plazas are examined. This preliminary study suggests that subethnic identification determines business practises in Chinatown by informing the types of networks one has access to, and participation in events such as the “Eat, Shop & Celebrate at Downtown Chinatown ” tourism promotion. Business trajectories in Chinatown rest on a combination of strong family cooperation, multilingual ability and fluid ethnic identities. Multilingualism in English, Vietnamese, Cantonese, and Mandarin facilitates better customer and supplier relations, and also widens the networks available to access business information and advice, such as which products are popular, where to get products at lower cost, availability of cheap property, and flexible credit arrangements. Family support, labour and advice remain important conduits to business success in Chinatown. [draft only: please do not quote without author’s permission] 1 Sino-Vietnamese: Chinese sub-ethnic relations in Toronto’s
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".