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Record W2189259067 · doi:10.1371/journal.pone.0143469

What’s in a Friendship? Partner Visibility Supports Cognitive Collaboration between Friends

2015· article· en· W2189259067 on OpenAlexafffund
Allison Brennan, James T. Enns

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFriendshipVisibilityCognitionPsychologySocial psychologyPartition (number theory)Developmental psychologyMathematicsGeography

Abstract

fetched live from OpenAlex

Not all cognitive collaborations are equally effective. We tested whether friendship and communication influenced collaborative efficiency by randomly assigning participants to complete a cognitive task with a friend or non-friend, while visible to their partner or separated by a partition. Collaborative efficiency was indexed by comparing each pair's performance to an optimal individual performance model of the same two people. The outcome was a strong interaction between friendship and partner visibility. Friends collaborated more efficiently than non-friends when visible to one another, but a partition that prevented pair members from seeing one another reduced the collaborative efficiency of friends and non-friends to a similar lower level. Secondary measures suggested that verbal communication differences, but not psychophysiological arousal, contributed to these effects. Analysis of covariance indicated that females contributed more than males to overall levels of collaboration, but that the interaction of friendship and visibility was independent of that effect. These findings highlight the critical role of partner visibility in the collaborative success of friends.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.176
GPT teacher head0.366
Teacher spread0.190 · 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 designObservational
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

Citations24
Published2015
Admission routes2
Has abstractyes

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