Doctoral Graduates in Canada and the United States: Who Goes to North America for a Degree and Why
Bibliographic record
Abstract
The paper uses census data to examine employment and salaries of doctors in Canada and the U.S., as well as their mobility between the two countries. The main conclusions are: 1) the percentage of employment of doctors in the U.S. is significantly higher than in Canada, while in the U.S. educational sector, their concentration is significantly lower than in Canada; 2) income of doctors in the U.S., both in absolute terms and as the growth pace in the 1990's many times greater than earnings their Canadian colleagues; 3) an intense mobility of PhDs takes place between Canada and the USA; 4) in Canada compared to U.S. there are a higher percentage of doctors of foreign origin, which, however, does not cause a significant difference in the income of doctors between these two countries; 5) the most likely cause of such a gap - in slower growth in demand for doctors in Canada than in the U.S., 6), the gap between incomes of doctors of Canada and the United States increased over 1990 despite their considerable labor mobility. Possible explanations: a difference in the quality of training of doctors, lower incomes for those doctors who have recently immigrated to the United States, a strong dedication to their country of Canadian doctors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".