MétaCan
Menu
Back to cohort
Record W1492537368

The Negotiator as Professional: Understanding the Competing Interests of a Representative Negotiator

2007· article· en· W1492537368 on OpenAlexaff
Trevor C. W. Farrow

Bibliographic record

VenuePepperdine Digital Commons (Pepperdine University) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsYork University
Fundersnot available
KeywordsNegotiationExpansivePolitical sciencePerspective (graphical)Law and economicsPublic relationsAlternative dispute resolutionLawDispute resolutionSociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This article is about lawyers as negotiators, and in particular, it is about identifying and understanding the influential and potentially competing interests that are - or at least should be - in the minds of lawyers (and potentially other third party representatives) during the overall negotiation process. While there continues to be an increasing amount of literature on the mechanics and strategies of negotiation, the underlying interests that are typically at stake in representative negotiations from the perspective of representatives - particularly negotiations involving lawyers - have not been adequately studied. Current accounts of the representative negotiator do not paint a full picture of what is typically going on inside the representative's mind, and as such, provide an impoverished view of his or her role, both in terms of its responsibilities and its potential opportunities. To address these deficiencies, this article advances an alternative, expansive model of the representative negotiator: the negotiator-as-professional model. It is a model that sees the role of the representative negotiator as being defined by at least four sets of interests: client interests, a broad understanding of the representative's self-interests (that may include, but are not limited to, interests vis-a-vis the representative negotiator's bargaining opposite), ethical interests and the public's interests.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.051
GPT teacher head0.357
Teacher spread0.306 · 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.

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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePepperdine Digital Commons (Pepperdine University)Same topicLegal Education and Practice InnovationsFrench-language works237,207