Do race and gender matter in international assignments to/from Asia Pacific? An exploratory study of attitudes among Chinese
Bibliographic record
Abstract
Abstract Based on a survey of EMBA students in China and South Korea, this article examines how two sensitive but potentially salient criteria—race and gender—affect the selection of an executive to head the (a) foreign operations of a U.S. multinational in China and South Korea and (b) newly acquired U.S. operations of a Korean multinational. The results reveal a fairly complex picture of how gender, race, and the interplay of these two factors might affect these decisions. In the Korean sample, competencies mattered more than race and gender in a senior executive appointment to the U.S. operations of Korean multinationals. Also in the Korean sample, race and gender outweighed competencies in assignments to Korea. In the Chinese sample, competencies outweighed race and gender in a senior executive appointment in China. Overall, Koreans had a more positive attitude toward foreign‐born Koreans than the Chinese toward foreign‐born Chinese for senior executive appointments. Implications for international human resource management and diversity management, both theoretical and applied, are discussed. © 2008 Wiley Periodicals, Inc.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".