Syrian, Armenian, and Lebanese Claims to Whiteness in Post-War Canadian Immigration Policy
Bibliographic record
Abstract
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".