Stratification and Synergy: Navigating Social Class Dynamics in Contemporary Management
Bibliographic record
Abstract
The symposium makes substantial contributions toward elucidating the influence of social class in the workplace. From exploring the social costs of upper-class networks to the trust dividends of upward mobility, the impact of class on creativity, and the intersection of founder and investor class origins, each study offers fresh insights into longstanding debates. Together, they challenge and refine our understanding of social class as a critical dimension of management, providing empirical evidence and theoretical advancements that pave the way for more nuanced approaches to leadership and organizational strategy in an era of increasing socioeconomic awareness. The Social Costs of Upper-Class Networks: Network Social Class Reduces Prosocial Behavior Author: Jiyin Cao; Chinese U. of Hong Kong Author: Siyu Yu; U. of Michigan Upward Mobility Increases Trust Author: Nadav Klein; INSEAD Author: Stephane Côté; U. of Toronto Novelty vs. Usefulness? Examining Social Class Differences in Creativity Author: Jiyoun Kim; Northwestern Kellogg School of Management Author: Peter Belmi; U. of Virginia Privilege or Humble Beginnings? Founder and Investor Social Class Origins Affect Investor Interests Author: Jean Joohyun Oh; Carnegie Mellon U. - Tepper School of Business
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".