Getting Real: Enhancing the Acquisition of Negotiation Skills Through a Simulated Email Transaction
Bibliographic record
Abstract
The authors first review the widespread use of role-plays and simulated exercises in response to the challenges of learning and teaching negotiation – in particular, the need to integrate theoretical and skills-based instruction. Drawing on their experience in the classroom, they present a simulation adapted to redress common deficiencies of role-playing.Law students in different provinces were paired to negotiate a commercial transaction between law firms. Working with basic background facts and a stranger on the other side, the students were free to use their own names and choose their own negotiating styles, thereby reducing the artificiality experienced by role-players who have to assume roles and pretend not to know their counterparts. The exercise allowed for the development of a negotiation relationship over the course of a week, in contrast to the one-time nature of many in-class simulations. The negotiation also took place solely by email and invited students to explore the impact of this mode of communication.Using excerpts from student learning journals, the authors discuss the results under six headings: (i) initiating the relationship and managing written communication; (ii) making fundamental choices and dealing with dilemmas; (iii) adapting strategy; (iv) attempting to influence outcomes through anchoring; (v) managing information; and (vi) constructing negotiator identity. The authors conclude from the degree of student reflection and critical thinking that such simulations can contribute to better integration of skills and theory, and perhaps even to transformational learning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".