Chinese racialisation and colonial complicity: connecting capitalism, white supremacy and settler colonisation in Aotearoa
Bibliographic record
Abstract
Building on theorisations examining the colonial positionalities of Asian diasporic communities in settler societies such as Canada, Hawai’i and the U.S., this article investigates the role of Chinese racialisation and discursive positioning vis-à-vis Pākehā and Māori in bolstering, obscuring or otherwise entrenching White supremacy, settler colonisation, and capitalism in Aotearoa. Specifically, I offer a critical analysis of historical texts, building upon and expanding existing narratives within secondary sources through the theoretical framework of colonial racial capitalism. I argue that Chinese racialisation and discursive positioning in Aotearoa has and continues to be shaped by the material interests of ‘New Zealand’ as a settler-colonial project founded upon the subjugation of Māori land and life, and reliant on the alienation of racialised tauiwi from the national identity and body politic. At once a Yellow Peril, model minority and essentialised Other, Chinese immigrants have been differentially racialised and positioned in society in order to produce and reproduce the conditions necessary for the preservation of colonial racial capitalism. Our racialisation as Chinese people can then be understood as inseparable from the settler-colonial project, and so to resist against White supremacy thus also requires that we resist against the violences of capitalism and (settler) colonisation.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".