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Record W2921736597 · doi:10.1111/ncmr.12155

Normatively Speaking: Do Cultural Norms Influence Negotiation, Conflict Management, and Communication?

2019· article· en· W2921736597 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNegotiation and Conflict Management Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNegotiationConformityConflict managementNorm (philosophy)NormativeSocial psychologyConflict resolutionCultural diversityCultural conflictPsychologySociologyPublic relationsPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

Abstract This paper elaborates a research agenda on cultural norms in communication, negotiation, and conflict management. Our agenda is organized around five questions on negotiation and conflict management, for example: How do culture and norms relate to an individual's propensity to negotiate? Or How do tightness‐looseness norms explain negotiators’ reactions to norm conformity and norm violation? And three questions on communication, for example: What individual and cultural factors lead negotiators to use miscommunication as an opportunity rather than an obstacle? Or Are there cultural differences in whether and what forms of schmoozing are normative? The present paper is based on three pillars: (a) ideas provided by the think tank participants (full list on website), (b) state of the art research and (c) the authors’ perspectives. Our goal is to inspire young, as well as, established researchers to purse these research streams and increase our understanding about the influence of cultural norms.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.059
GPT teacher head0.387
Teacher spread0.328 · 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