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Record W1565934189

Getting Real: Enhancing the Acquisition of Negotiation Skills Through a Simulated Email Transaction

2011· article· en· W1565934189 on OpenAlexaff
John C. Kleefeld, Michaela Keet

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNegotiationRedressClass (philosophy)Database transactionPsychologyIdentity (music)Social psychologyComputer sciencePublic relationsPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.332
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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