Exclusion through Inclusion: Female Asian migration in the making of Canada as a white settler nation
Bibliographic record
Abstract
In this article I explore the ways in which concerns over nation, ‘race’, gender and sexuality shaped late nineteenth and early twentieth century debates on whether Canada should exclude or include female migrants from China, Japan and India in the emerging nation-state. In the late nineteenth century, Canadians began to debate whether to allow female migration from China, Japan and India. The vast majority of those who participated in the debate argued that female migrants from Asia should be excluded, as their exclusion would insure that male migrants from Asia would be rendered as temporary residents. On the other hand, there was a small but vocal minority who argued that female migrants from Asia should be allowed into Canada. As the presence of single male Asian residents raised the specter of inter-racial sexuality, these Canadians suggested that it would be prudent to include female migrants from Asia within the nation-state. These debates raise important questions for scholars who study the relationships between nation, ‘race’, gender and sexuality. First, they point once again to the importance of gender in constituting the racialized practices of the nation. Second, as most scholars have focused on the exclusionary aspects of nationalism, they complicate our understanding of race, gender and nation by illustrating that racialized politics of nation can lead to not only exclusionary but also inclusionary practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.049 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".