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Information sharing as story construction in group decision making

2015· article· en· W2346382157 on OpenAlexaff
Lu Xiao, Richelle Witherspoon

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of New BrunswickWestern University
Fundersnot available
KeywordsPoolingGroup decision-makingStorytellingJuryGroup (periodic table)MediationProcess (computing)Computer scienceInformation sharingPsychologySocial psychologyKnowledge managementArtificial intelligenceSociologyWorld Wide WebNarrativeLinguisticsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Prior research in group decision‐making has shown that group members tend to share and focus on the information that is known to the majority of the group but keep the unique information unshared. Tasks created to study this information pooling phenomenon are referred to as hidden profile tasks. A recent hidden profile experiment showed that group members constructed stories to reach their group decision. The study discouraged this storytelling approach and suggested that technology mediation could provide a way to reduce the likelihood of using this approach in discussion. While our experiment confirmed this story construction approach, we found that in the story construction process the participants considered the important arguments as well as different perspectives. We therefore suggest that the story development approach is rational and that the Story model, an existing group process model well‐documented in jury decision making literature, could shed light on the design of collaborative technologies that accommodate or improve such a discussion approach.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.293
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.012
GPT teacher head0.290
Teacher spread0.277 · 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.

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
Published2015
Admission routes1
Has abstractyes

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