JOURNAL OF MANAGEMENT EDUCATION / June 2002 Thompson / CHINESE PERSPECTIVES CHINESE PERSPECTIVES ON THE IMPORTANT ASPECTS OF AN MBA TEACHER
Bibliographic record
Abstract
The marketization of China has led to an increased demand for manage-ment education in that country. This demand is increasingly being met by the supply of MBA and other business courses broadly modeled on those offered in North America and Europe. Ensuring the success of such programs plainly requires some modification of course content to account for the peculiarities of China’s business environment. However, differences between Eastern and Western learning traditions also suggest that teaching approaches may also need alteration. Key to ensuring such alteration is an appropriate understand-ing of what Chinese students regard as the important aspects of an MBA teacher. This article uses data from 113 Chinese students to highlight what these aspects are. MANAGEMENT EDUCATION DEMAND AND SUPPLY IN CHINA China is poised to be the world’s largest new market for management edu-cation in the next few decades (Johnstone, 1997; Kamis, 1996). Some esti-mates suggest that by 1996, foreign joint ventures in China alone needed around a quarter of a million professionally trained managers, around one 229 Author’s Note: The author is grateful to two anonymous referees for useful comments on two earlier drafts of this article. The assistance and comments of Qin Gui and Florence Phua are also gratefully acknowledged. The data on which this article is based were collected while the author was a faculty member of Hong Kong University School of Business, support from which 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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".