Variety, Dissimilarity, and Status Centrality in MBA Networks: Is the Minority or the Majority More Likely to Network Across Diversity?
Bibliographic record
Abstract
The value of the networks that MBA students develop is often limited by the tendency of people to favor connections with similar others, resulting in self-segregation among identity groups. To identify the origins of network diversity, a key question for theory and practice is whether majority or minority groups are more likely to develop diverse personal networks. We provide a partial answer to this question by integrating network theory with three conceptual dimensions of diversity: variety, dissimilarity, and status. This conceptualization suggests that individuals can display three distinct types of diversity in their networks with different theoretical antecedents and outcomes. Consistent with theoretical predictions, we find systematic differences between the networks of high-status majorities and low-status minorities in a longitudinal study of MBA student networks. Specifically, minorities show more variety, greater dissimilarity, and lower status centrality in their networks compared to majorities. Tie strength and time period affect the findings in predictable ways. These results demonstrate the value of integrating diversity theory with network theory for understanding the development of inclusive networks in business schools. We conclude by discussing potential remedies to enhance the diversity of MBA student networks.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".