Variety, Dissimilarity, and Status Centrality in MBA Networks: Is the Minority or the Majority More Likely to Network Across Diversity?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it