Cultural Identity within the Chinese Community in Toronto Examined Through the Chinese Orchestra - A Study on the Toronto Chinese Orchestra
Bibliographic record
Abstract
Ethnic Chinese comprise the second largest visible minority group in Canada, making up \n21% of Canada’s visible minority population and 4% of its total population (StatsCan 2011). \nThey consistently rank as one of the three largest groups immigrating into Canada. According to the national household survey taken in 2011, over 70% of all Chinese Canadians live in two cities (40.1% in Toronto and 31.1% in Vancouver) (Ibid. 16). Despite their large population and a field of literature on the topic of Chinese Canadians, there is surprisingly little written on their musical activity. Similarly, research on Chinese diasporic music is also limited despite the prevalence of studies on the Chinese diaspora and Chinese music individually. This major research paper will look at the cultural identities in the Chinese community in Toronto through the development of its Chinese orchestral activities. The paper will examine specifically the identity of the Toronto Chinese Orchestra, the longest running Chinese orchestra in Canada and the largest in Ontario. \nThe paper will comprise of three main sections: \n1) Overview of the history and development of the modern Chinese orchestra as a vehicle to \nexpress cultural identity within the Chinese ethnicity in the twentieth century \n2) Overview of the history and development of the Chinese orchestra in Toronto in relation \nto Chinese migration \n3) Analysis on the cultural identity of Toronto Chinese Orchestra, based on its activities and \nrepertoire
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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 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".