Between Global Norms and Domestic Institutions: Postwar Immigration Policymaking in Canada and the United States
Bibliographic record
Abstract
Although both Canada and the United States are self-declared immigration countries, their means of regulating admissions are quite different. Whereas the United States privileges family reunification, Canada's "points system" grants policymakers greater flexibility in tailoring immigration flows to meet changing economic needs. This paper explores the origins of these distinct approaches. I argue the two states' policies have similar roots: In the post-World War II era, changing norms ertaining to race, ethnicity, and human rights cast longstanding discriminatory policies in Canada and the United States in a highly critical light. Opponents of racial discrimination in immigration policy took advantage of this new normative context to highlight the lack of fit between Canada and the United States' commitment to liberal norms and human rights and their extant policy regimes. This pressure set in motion comparable processes of policy "stretching" and "unraveling," which culminated in policy "shifting" in the mid-1960s. Processes of policy change were, however, subject to quite different political dynamics. Canada's institutional configuration granted the executive branch and bureaucracy a high degree of autonomy; policy change therefore accorded to models of elite learning. Conversely, the greater openness of the American political system and the pivotal role of Congressional committees led to a more politicized process. As a result, the executive branch's efforts to recast immigration policy in conomic terms, as in Canada, failed. The result was a patchwork policy that aimed to mol1ify distinct and conflicting interests. Thus, while Canada and the United States both replaced discriminatory policies with more liberal alternatives, the objectives of their respective policies were quite different.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".