Race Matters: Unpacking the Influence of Racial Identity on Negotiation Outcomes
Bibliographic record
Abstract
This symposium features four papers that explore the role of race in negotiations. The studies utilize diverse methodologies and data sets to demonstrate gaps in negotiation propensity and outcomes, both between and within racial groups; the nuanced underlying mechanisms through which these gaps manifest, such as through differential employer assessment across racial groups; and initial evidence for strategies that may mitigate racial disparities in negotiation experiences. The goals of this symposium are to highlight ongoing research in the under- studied area of race and negotiations, and advance diversity and negotiations scholarship by illuminating the ways in which racial identity influences multiple stages of the negotiation process. Racial Discrimination in Online Job Negotiations Author: David P. Daniels; NUS Business School The Influence of Race and Sexual Orientation on Negotiation Outcomes For Men Author: Edward Chang; Harvard Business School Asians Don’t Ask? Relational Concerns, Negotiation Propensity, and Starting Salaries Author: Jackson Lu; MIT Sloan School of Management Fit in or Stand Out? The Effectiveness of Race-Based Impression Management in Salary Negotiations Author: Kathy Vo; Kellogg School of Management, Northwestern U. Author: Gabrielle Rose Lopiano; Vanderbilt U. Author: Tosen Nwadei; U. of Toronto, Rotman School of Management
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".