Fostering Connection, Managing Tension: Navigating Difficult Conversations in Organizations
Bibliographic record
Abstract
Despite the extant research highlighting the benefits of having difficult conversations, its inherent complexity – particularly due to the interdependent, multimodal, and highly contextualized nature of conversation – has impeded its empirical advancement and theoretical integration. Furthermore, previous research has assumed that having, or being able to have, difficult conversations is invariably beneficial for individuals, teams, and organizations. However, exactly how these conversations unfold and lead to positive outcomes remain a mystery. In this symposium, five presentations will explore why and how particular conversational elements within difficult contexts, such as grief, distrust, conflict, diverging goals, and advice giving and seeking, may lead to better or worse outcomes for individuals in organizational settings. In total, the symposium offers empirical and theoretical insights into the burgeoning science of conversation research, as well as practical solutions for managers, leaders, and employees who wish to create spaces where people are heard and feel connected to others. Expressions of Sympathy are Less Effective When They Focus on the Positive Author: Daniel Chiacchia; U. of Toronto, Rotman School of Management Author: Rachel Lise Ruttan; U. of Toronto Author: Katherine Ann DeCelles; U. of Toronto Author: Sora Jun; Rice U. Communicating Under Distrust Author: Laura Wallace; U. of Chicago Booth School of business Author: Yena Kim; U. of Chicago Booth School of business Author: Emma Levine; U. Of Chicago The Social Effects of Discrete Emotions on Curiosity Elicited During Conflict Author: Christina Bradley; U. of Michigan, Ross School of Business Author: Nadav Klein; INSEAD Boomerasking: Answering Your Own Questions Author: Alison Wood Brooks; Harvard U. Author: Michael Yeomans; Imperial College Business School Flattering Advice: Avoiding Disappointment as a Driver of Gender Discrimination Author: Zaidan Chen; Hong Kong U. of Science and Technology Author: David Hagmann; -
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".