Normatively Speaking: Do Cultural Norms Influence Negotiation, Conflict Management, and Communication?
Bibliographic record
Abstract
Abstract This paper elaborates a research agenda on cultural norms in communication, negotiation, and conflict management. Our agenda is organized around five questions on negotiation and conflict management, for example: How do culture and norms relate to an individual's propensity to negotiate? Or How do tightness‐looseness norms explain negotiators’ reactions to norm conformity and norm violation? And three questions on communication, for example: What individual and cultural factors lead negotiators to use miscommunication as an opportunity rather than an obstacle? Or Are there cultural differences in whether and what forms of schmoozing are normative? The present paper is based on three pillars: (a) ideas provided by the think tank participants (full list on website), (b) state of the art research and (c) the authors’ perspectives. Our goal is to inspire young, as well as, established researchers to purse these research streams and increase our understanding about the influence of cultural norms.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".