Can’t We Just Talk about This? New Insights into Difficult Conversations
Bibliographic record
Abstract
The current symposium provides new insights into difficult conversations. We first highlight which conversations people find difficult, looking at whether people want to initiate sensitive conversations or wait to confess something. We then discuss other factors beside the topic of the conversation that can make conversations difficult. Different class backgrounds might create anxiety for the interlocutors and having different mindsets in a conversation might lead to different outcomes. However, with all these difficulties of having a conversation with someone who is different from you, we show that people still try to find opportunities to talk about difficult topics. What Are You Waiting For? Delaying a Confession Does Not Help Presenter: Katherine Qianwen Sun; Columbia Business School Presenter: Michael Slepian; Columbia Business School Interacting Across Class Lines: Who is Threatened in Cross-Class versus Same-Class Interactions? Presenter: Mindy Truong; Northwestern Kellogg School of Management Presenter: Sarah S M Townsend; U. of Southern California Presenter: Nicole Stephens; Northwestern U. Dialogue vs. Debate: Causes and Consequences of Two Approaches to Disagreement Presenter: Kristina Wald; U. of Chicago Booth School of business Presenter: Anastasiya Apalkova; U. of Chicago Booth School of business Presenter: Xuan Zhao; Stanford U. Presenter: Heather M. Caruso; UCLA Anderson School of Management Presenter: Jane Risen; U. of Chicago Booth School of business “If it’s easy, you’re doing it wrong:” Managing discomfort in conversations about allyship Presenter: Olivia Foster-Gimbel; New York U.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".