Graduate education in Canada and China: What enrolment data tells us
Bibliographic record
Abstract
China’s emergence as a global economic and political power is in part due to the country’s renewed involvement with, and commitment to, graduate higher education (Harris, 2005). Graduate education in China is viewed as the means of producing the essential scientists, engineers and skilled workforce needed to sustain the country’s rapid industrial growth and economic development. But how does China’s graduate education system compare with North American graduate higher education and what can each learn from the other? This paper examines the trends and patterns in Master’s level graduate education programs in China and Canada based on enrolment data gathered from 1999 to 2005. Initial comparisons of the data find that Master’s level enrolments in China are growing faster than in Canada; enrolment pattern distributions for both countries are unbalanced geographically and from a disciplinary perspective the highest number of Master’s level enrolments in Canada were in the business and management disciplines while in China the greatest Master’s level enrolments were in engineering. The comparisons provided by this study help identify some of the trends and challenges of graduate education at both the national and the regional levels of both countries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".