Canadian Journal of Sociology Online March-April 2007 Review Essay Canadian and U.S. Immigration Policies: Divergence within Convergence
Bibliographic record
Abstract
Canada and the United States have followed parallel trajectories in immigration. Both countries initially drew on the British Isles as the primary source of immigration to North America, gradually expanding their catchment area into the north, south, and then east of Continental Europe. This “old immigration, ” which lasted until about the late 1960s, was highly homogeneous – geographically European, racially White, religiously Christian, and economic in motivation. The final removal of racial preferences from both countries ’ immigration policies in the second half of the 1960s paved the way for the “new immigration. ” Unlike its predecessor, the new immigration to North America was defined by a high degree of heterogeneity. The former Third World replaced Europe as the main source area, bringing with it geographic, racial, and religious diversity, as well as a significant refugee component. Within this shared context, however, Canada and the United States are separated by a “continental divide ” to borrow from Seymour Martin Lipset (1990). Since the reforms of the 1960s, Canadian and U.S. immigration policies diverged in significant ways. Whereas Canadian policy became more skill-selective, U.S. policy emphasized family reunification (see Borjas 1999; but also Reitz 1998). As the leader of the “free world ” during the Cold War, the United States was also heavily involved in
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".