Cantonese migrant networks, white supremacy, and the political utility of apologies in Canada
Bibliographic record
Abstract
Using the specific example of the attempts of the municipal government of Vancouver in 2018 and the provincial government of British Columbia in 2014 to apologise for historical anti-Chinese legislation in Canada, this paper illustrates how Cantonese migrant networks shaped and were shaped by the politics of white supremacy in Pacific white-settler nations, and argues that historical narratives about historical anti-Chinese discrimination that erupt at these moments of apology and reconciliation aspire to shape future politics by producing historical frames for civil society that are broadly inclusive and based primarily neither on ethnic identification nor shared exclusion in the past but on aspirational narratives of more inclusive belonging in the future. They also provide at the same time a narrative of belonging for migrants who trace ancestry to very different parts of China and with divergent identities and migratory pathways to identify with each other’s histories and to belong to a ‘Chinese’ diasporic past and future marked by resistance to racism and white supremacy in Pacific settler colonies such as Canada, the United States, Australia, and New Zealand. Included in this future are Chinese who were excluded in the past, allowing those who identify as Chinese a path for belonging.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.046 | 0.019 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".