Relating Across Differences: Tools for Repairing Breakdowns in Interpersonal Understanding
Bibliographic record
Abstract
Interpersonal differences abound in the workplace. Reaping the benefits of organizational diversity requires understanding and appreciating one another’s different backgrounds, worldviews, perspectives, and viewpoints. However, the innate ease and preference associated with interacting with similar others makes relating across differences challenging in a number of ways. The five papers in this symposium provide cutting-edge insights into breakdowns in understanding that can occur across interpersonal differences in organizations, including both the emotional and cognitive processes underlying these breakdowns, as well as potential tools to prevent and repair them. The Illusion of Rapport: How Coercive Power Disparities Undermine High-Quality Connections Author: Andrea Dittmann; U. of Southern California - Marshall School of Business Author: Kyle Dobson; U. of Virginia Author: David Yeager; The U. of Texas at Austin The Role of Identity in Relationships with Neurodivergent Colleagues Author: Natalie Longmire; Tulane U. Author: Niranjan Srinivasan Janardhanan; London School of Economics Intellectual Humility Predicts Empathic Accuracy and Empathic Resilience Author: Michal Lehmann; Carnegie Mellon U. - Tepper School of Business Author: Shir Genzer; Hebrew U. of Jerusalem Author: Nur Kassem; Hebrew U. of Jerusalem Author: Daryl R. Van Tongeren; Hope College Author: Anat Perry; Hebrew U. of Jerusalem Everyday People, Rare Relationships: Microemancipation in Racialized and Gendered Organizations Author: Jennifer Wiseman; U. of Utah, David Eccles School of Business Author: Jared Mitchell Poole; U. of Massachusetts, Boston Author: Amelia Stillwell; U. of Utah Conspiratorial Beliefs and COVID-19 Vaccine Acceptance: The Role of Perspective-Taking Author: Yingli Deng; Durham U. Business School Author: Hooria Jazaieri; Santa Clara U. Author: Cynthia S. Wang; Northwestern Kellogg School of Management Author: Jennifer Ann Whitson; U. of California, Los Angeles
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".