Canada’s Invisible Nationality Policy: Creating Ethnicity, Managing Populations, Imagining a Nation
Bibliographic record
Abstract
This article explores the process of national identity construction at Canadian borders in the 1930s. Based on the examination of customs declaration forms (transatlantic ship manifests) of arriving passengers at the port of Quebec in the first half of the 1930s, as well as Royal Canadian Mountain Police (RCMP) files, I suggest that the Canadian bureaucracy, informed by social Darwinist views of race and ethnicity, and the Anglophone bourgeois concern over modernization brought upon by the forces of industrialization and urbanization, developed an elaborate system of categorization of Canada’s population according to prescribed criteria of ethnicity, nationality, and race. Utilizing critical discourse analysis, I argue that in efforts to “know” and control its growing population, the Canadian state developed a rigid, albeit an invisible nationality policy. Although there was never an official nationality policy in place in Canada in the 1930s, public officials, the media, and security agencies not only operated with an acute awareness of national and ethno-racial differences in the society, but also worked to reinforce such divisions in attempts to maintain the social order and the cultural, social, and economic status quo. In this work, I imply that border officials were directly responsible for constructing specific representations of Canada’s ethnic populations, all within the context of an impending need to control the population of a rapidly modernizing society, where the Canadian community had to be made “knowable,” familiar, and recognizable in the official discourse.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.050 | 0.050 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".