Influences on selecting executives: the case of gender and race in managerial decisions in Taiwan
Bibliographic record
Abstract
In the global war for talent, the importance of selecting the 'best' person irrespective of race and gender is often noted. In this paper, we examine attitudes towards gender and race in respect of who are seen as the 'best' candidates for executive positions. Taiwanese managers selected the 'best' candidate for the post of Director of a variety of multinational company (MNC) operations. We found the following. For the Taiwanese operations of a US MNC where the most managerially and technically qualified candidate was portrayed as an African-American female, she was ranked second, although first by respondents from middle and junior management positions. Where the most qualified candidate was portrayed as a white American female, she was ranked first. In contrast, for the Director of US operations of a Taiwanese MNC, the African-American female was ranked first. Some possible reasons for these findings and their implications are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".