Sociocultural factors matter: the effects of gender and racio-ethnicity on attributions of character and associated behaviors
Bibliographic record
Abstract
We examined whether the gender and racio-ethnic backgrounds of individuals affect the perceived importance of virtuous character in achieving success in a leadership role in business. Our study was motivated by the concern that the discipline of positive psychology—the foundation of virtuous character—largely neglects the role of context in understanding positive characteristics in the workplace. We used a survey involving 835 experienced managers to explore our research question. ANOVAs and independent sample t-tests revealed that participants rated character and its associated virtues and character strengths as high in importance for achieving success. However, the ratings were affected by gender, racio-ethnic background, age, work experience, managerial experience, education, and household income. The findings have important implications for people management processes in the workplace. We hope that our study is a step in the right direction toward contributing to the conversation of whether the virtues and character strengths identified through the research program by Christopher Peterson and Martin Seligman and adopted by scholars in management and organization are truly universal. Our results reinforce the need for studies that expand on intersectionality and contextual influences to refine our understanding of character in diverse leadership settings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".