The Changing Faces of Chinese Canadians: Interpellation and Performance in the Deployment of the Model Minority Discourse
Bibliographic record
Abstract
The history of Chinese settlement in Canada is one that closely parallels the evolution of the Canadian states own racial and immigration policies. As policy shifted from covert and overt forms of racial exclusion and discrimination, including the Chinese Immigration Act of 1923 that attempted to ban immigration from China altogether, to the introduction of an official multicultural policy and a points system that admitted prospective immigrants based upon their academic and economic credentials, the portrayal of Chinese Canadians has centred on two predominant stereotypes: the Yellow Peril and the Model Minority. \n \nWhile it is easy to retroactively assume that the Yellow Peril discourse has been superseded by that of the Model Minority particularly in light of Canadas official multiculturalism policy, the increased economic and social capital of Chinese Canadians, and Chinas own recent economic boom this dissertation argues instead that both discourses have co-existed since the beginning of Chinese immigration to Canada, and continue to do so today. \n \nUsing a combined examination of Chinese Canadian history and life writing, I argue that the Model Minority discourse is not a recent phenomenon; rather, it is an example of the complex relationship between external interpellation by mainstream Canadian society, and the agency and affective performance of Chinese immigrants and their descendants. While the Model Minority discourse has been used as a tool to maintain the Eurocentrism of mainstream Canadian society by placing Asian immigrants, including Chinese, upon a pedestal in contrast to other racialized minorities, it has also found footing in the desire of Chinese Canadian communities to be accepted and acknowledged as desirable citizens by the Canadian state and the public.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.025 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".