Watch Your Tone … Relational Paralinguistic Messages in Negotiation
Bibliographic record
Abstract
This study examines how East Asian and North American negotiators convey relational cues using vocal paralanguage. Drawing upon the involvement-affective model of relational messages, the authors posit that vocal cues in negotiation connote level of involvement (passive-active) and affect (positive-negative). Since cultural norms influence emotional expression, they predict distinct patterns of vocal paralanguage accompanying relational status in the East versus the West. The authors manipulated relational approach and examined vocal paralanguage in a videotaped business negotiation simulation in an undergraduate academic course at a Canadian university. Their findings confirm that Canadian negotiators communicate positive perception of counterpart and active involvement in negotiation through faster speech rate and expressiveness in voice. Chinese negotiators exhibit self-control by remaining calm and suppressing emotion in vocal tone. Furthermore, warmth in voice predicts satisfaction with relationship in negotiation, especially when a negotiator is not actively involved. Theoretical and practical implications for cross-cultural negotiation and communication 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".