What's a Masculine Negotiator? What's a Feminine Negotiator? It Depends on the Cultural and Situational Contexts
Bibliographic record
Abstract
Abstract Gender‐related categorization is a key feature of the literature on gender in negotiation. While previous literature focused on context‐free traits such as warmth and competence, we examine how people categorize specific negotiation goals and behaviors asmasculineandfeminine across theUnitedStates andChina in different negotiation contexts, illustrating the role of cultural and situational contexts in gender‐related categorization. Two studies found that whileAmerican participants categorized competitive goals and behaviors as masculine and cooperative ones as feminine across business‐to‐consumer (B2C) and business‐to‐business (B2B) negotiation contexts,Chinese participants' patterns depended on the negotiation context. InB2Ccontexts,Chinese participants categorized competitive goals and behaviors as feminine and cooperative ones as masculine; inB2Bcontexts, they made further distinctions, categorizing competitive goals and behaviors that are socially inappropriate as feminine, but competitive ones that are socially appropriate, and cooperative goals and behaviors, as masculine. Theoretical and practical implications are discussed.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".