Recent Immigration to Canada and the United States: A Mixed Tale of Relative Selection
Why this work is in the frame
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Bibliographic record
Abstract
Using large-scale census data and adjusting for sending-country fixed effect to account for changing composition of immigrants, we study relative immigrant selection to Canada and the U.S. during 1990-2006, a period characterized by diverging immigration policies in the two countries. Results show a gradual change in selection patterns in educational attainment and host country language proficiency in favor of Canada as its post-1990 immigration policy allocated more points to the human capital of new entrants. Specifically, in 1990, new immigrants in Canada were less likely to have a B.A. degree than those in the U.S.; they were also less likely to have a high-school or lower education. By 2006, Canada surpassed the U.S. in drawing highly-educated immigrants, while continuing to attract fewer low-educated immigrants. Canada also improved its edge over the U.S. in terms of host-country language proficiency of new immigrants. Entry-level earnings, however, do not reflect the same trend: recent immigrants to Canada have experienced a wage disadvantage compared to recent immigrants to the U.S., as well as Canadian natives. One plausible explanation is that, while the Canadian points system has successfully attracted more educated immigrants, it may not be effective in capturing productivity-related traits that are not easily measurable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it