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Record W4389737526 · doi:10.1017/nps.2023.82

Syrian, Armenian, and Lebanese Claims to Whiteness in Post-War Canadian Immigration Policy

2023· article· en· W4389737526 on OpenAlexaffabout
Vic Satzewich, Leili Yousefi

Bibliographic record

VenueNationalities Papers · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcMaster University
FundersUniversity of Cambridge
KeywordsImmigrationPolitical sciencePoliticsImmigration policyContext (archaeology)Government (linguistics)Immigration reformWhite (mutation)ArmenianImmigration lawWorld War IILawPublic administrationHistoryAncient history

Abstract

fetched live from OpenAlex

Abstract This article asks how, in early post-World War II Canada, Syrian, Armenian, and Lebanese communities claimed whiteness in the context of Canada’s racially restrictive immigration regulations that defined them as “Asiatics,” and hence inadmissible. But, it also examines how Canadian politicians and immigration bureaucrats responded to those claims. Using so-far untapped archival records, this article shows that immigration authorities were unwilling to redefine the racial status of these groups out of fear that doing so would provide a wedge for other groups of “Asiatics” to press for the ability to migrate to Canada. In this case, Syrians, Armenians, and Lebanese could be regarded as experiencing collateral damage in the politics of whiteness. While Canadian immigration authorities seemed to privately accept the white/European identity claims of these groups, they were nonetheless unwilling to publicly grant them one of the privileges of whiteness – namely the ability to migrate to Canada on a basis equal to that of other white immigrants. Instead, the government used “merit-based” orders-in-council as an under the radar administrative mechanism to admit members of these groups. This allowed the government and the immigration department to avoid a larger public debate about racial discrimination against “Asiatic” immigrants.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0340.011
Scholarly communication0.0080.001
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.300
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2023
Admission routes2
Has abstractyes

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