On Training Excellent Students in China and the United States
Bibliographic record
Abstract
In many countries, the training of researchers who will be internationally competitive has become a primary objective, leading to extensive discussion of the curricula, educational content, and methods that may ensure a high level of student achievement. In this global climate, only the most excellent students have the potential to engage successfully in international competition and become leading-edge researchers in the world-wide marketplace of research. Thus, any country seeking to be internationally competitive must consider ways to further raise the level of excellent students. In this study, we investigate university programs, specifically honors programs, that take special measures for training the most excellent students. Honors programs can be found in the United States, Canada, Holland, China, Singapore, Chile, and other countries; among these, the highest number of honors programs are in the U. S. (Digby, 2005) and China. Consequently, the authors chose these two countries as the objects of this study, surveying and comparing the characteristics of honors programs as training courses for excellent students. In both countries, the focus of our study was limited to higher-level universities. In the case of China, only universities identified by Kitagaki & Fuang (2008) as “Key Chinese Universities” were investigated. A small sample of universities in the U. S. was selected from America’s Best Value Colleges (Owens & Meltzer et al., 2006). Our other major sources of information were university websites and the literature available through the National Collegiate Honors Council. In both China and the U. S., honors programs have a common aim to gather and train particularly excellent students in the universities while the specific content of each program and training course is distinct. The characteristics observed in the two countries as well as the comparison of such characteristics may help serve as models for Japan and other countries wishing to create honors programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".