Canadian Emigration to the United States
Bibliographic record
Abstract
This paper reviews the historical record of Canadian emigration to the United States and analyses recent changes in the economic incentives for emigration to the U.S., focussing on differences by education. For at least the past 60 years, Canadians living in the United States have been better-educated than those who remained in Canada. This pattern of selective emigration may have intensified recently. Two factors have contributed to rising economic incentives for emigration by better-educated Canadians: the decline in Canadian average incomes relative to those in the U.S., reflected in the fall of the Canadian dollar; and the sharp rise in relative wages of highly-educated young workers in the U.S.. *Thanks to David Y. Albouy for assistance with Canadian Census data, and to Charles Beach for generous editorial help. The research reported here is part of an ongoing project with Thomas Lemieux: I am extremely grateful for his collaboration and assistance with this paper. 1This figure is based on data in Zhao, Drew, and Murray (2000, Table 6). The 1850's decade refers to the period from 1851 to 1861. Rates are expressed relative to the population at the beginning of the decade. 2For example, nearly every Canadian can identify a list of prominent ex patriots living in the U.S.. It is
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".