MétaCan
Menu
Back to cohort
Record W2955941749 · doi:10.1111/ncmr.12163

Open for Learning: Encouraging Generalization Fosters Knowledge Transfer in Negotiation

2019· article· en· W2955941749 on OpenAlex
Jihyeon Kim, Leigh Thompson, Jeffrey Loewenstein

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 institutionsKellogg's (Canada)
Fundersnot available
KeywordsNegotiationOpenness to experienceSet (abstract data type)GeneralizationKnowledge managementPsychologyPublic relationsComputer scienceBusinessSocial psychologyPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract We examined whether encouraging managers to attend to underlying principles in negotiation training examples rather than contextual specifics fosters openness to learning and enhances subsequent knowledge transfer to new negotiation situations. In an experimental study, 420 managers read a negotiation case study example set in a familiar or unfamiliar industry and answered either broadening or narrowing questions about an example. Managers given broadening questions about an example set in an unfamiliar industry were more open to learning than managers who were asked narrowing questions about an example set in a familiar industry. Openness to learning in turn fostered successfully applying the key negotiation principle to resolve a subsequent face‐to‐face negotiation. The findings suggest that negotiation training for professionals is unlikely to meet its intended purpose if it relies on offering managers examples set in their own industries and encouraging them to answer questions about the contextual specifics of those examples.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.135
GPT teacher head0.452
Teacher spread0.317 · 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