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The Reality and Myth of Sacred Issues in Negotiations

2009· article· en· W2113850567 on OpenAlexaff
Ann E. Tenbrunsel, Kimberly A. Wade‐Benzoni, Leigh Plunkett Tost, Victoria Husted Medvec, Leigh Thompson, Max H. Bazerman

Bibliographic record

VenueNegotiation and Conflict Management Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsNegotiationContext (archaeology)AdversaryPerceptionSet (abstract data type)MythologySocial psychologyPsychologyEpistemologySociologyPolitical scienceComputer scienceLawHistoryPhilosophyComputer security

Abstract

fetched live from OpenAlex

Abstract This article investigates the role of sacred issues in a dyadic negotiation set in an environmental context. As predicted, a focus on sacred issues negatively impacts the negotiation, producing more impasses, lower joint outcomes, and more negative perceptions of one’s opponent; however, this is only true when both parties perceive that they have a strong alternative to a negotiated agreement. When negotiation parties perceive that they have weak alternatives, sacred issues did not have any effect on negotiation outcomes or opponent perceptions. These results suggest that the negative effects of sacred issues are driven in part by whether negotiators have recourse; in other words, exercising one’s principles and values may depend on whether people can afford to do so. We conclude by suggesting that the impact of certain sacred issues may be contextually dependent and that the term “pseudo‐sacred” may actually be a more accurate label for certain contexts.

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)0.014
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.047
Scholarly communication0.0140.010
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.087
GPT teacher head0.437
Teacher spread0.350 · 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 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

Citations13
Published2009
Admission routes1
Has abstractyes

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