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Record W3209085157 · doi:10.1080/1369183x.2021.1983957

Cantonese migrant networks, white supremacy, and the political utility of apologies in Canada

2021· article· en· W3209085157 on OpenAlexaffabout
Henry Yu

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWhite supremacyPoliticsWhite (mutation)Political scienceSociologyPolitical economyCriminologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.352
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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