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Record W3211911331 · doi:10.1111/nejo.12377

Teaching Entrepreneurial Negotiation

2021· article· en· W3211911331 on OpenAlexaff
Stephen E. Humphrey, Robert Macy, Cynthia S. Wang

Bibliographic record

VenueNegotiation Journal · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsNegotiationEntrepreneurshipClass (philosophy)Public relationsSociologyEntrepreneurship educationPolitical scienceComputer scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract Despite the importance of negotiation skills for entrepreneurs, the pedagogy of teaching negotiation to entrepreneurship students has not been fully developed. In some entrepreneurship programs, negotiation is covered briefly in a single class. In other programs, courses focused entirely on negotiation are available to entrepreneurship students; however, these classes are aimed primarily at those interested in pursuing corporate jobs within more stable environments. This article provides guidance to educators in designing, developing, and delivering negotiation content with an entrepreneurial focus. We explain why entrepreneurial negotiation education is needed and how it fills current gaps in entrepreneurship education. The article outlines a continuum of entrepreneurial negotiation, identifies the unique challenges faced by entrepreneurship students, unpacks the critical learning objectives in an entrepreneurial negotiation course, discusses how to reinforce core entrepreneurship concepts, and lays out a guide for teaching entrepreneurial negotiation by providing educational content that matches the key learning objectives.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0270.005

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations5
Published2021
Admission routes1
Has abstractyes

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Same venueNegotiation JournalSame topicFamily Business Performance and SuccessionFrench-language works237,207