MétaCan
Menu
Back to cohort
Record W4307358134 · doi:10.1163/15718069-bja10079

Justice and Negotiation: Themes and Directions

2022· article· en· W4307358134 on OpenAlexaff
Daniel Druckman, Lynn Wagner

Bibliographic record

VenueInternational Negotiation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsNegotiationSalience (neuroscience)ScholarshipEconomic JusticeDistributive justiceSociologyProcedural justiceMoralitySocial psychologyLaw and economicsPolitical scienceEpistemologyLawPsychologySocial sciencePerception

Abstract

fetched live from OpenAlex

Abstract This article examines how justice concerns arise during various stages of negotiation with attention paid to contending principles of procedural, distributive, and transitional justice. We review key themes raised by contributors to this special issue. The themes reveal that justice has many facets and surfaces in many contexts. The facets include the role played by voice, the utility of universal definitions of justice, the use of morality arguments, the salience of the equality principle, and the challenges of complex negotiating forums. The contexts vary from single to multiple case analyses. Looking forward, we suggest a number of issues for further research. These include the voice versus exit debate, culturally-sensitive definitions of justice, different forms taken by equality, and how best to develop the skills needed for implementing justice principles. These are a sampling of the issues that pave the way for future scholarship on the role of justice in negotiation.

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)0.022
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.024
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0090.046
Scholarly communication0.0240.026
Open science0.0030.012
Research integrity0.0060.009
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 head0.032
GPT teacher head0.325
Teacher spread0.293 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational NegotiationSame topicConflict Management and NegotiationFrench-language works237,207