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Record W2162419628 · doi:10.1177/104649640103200506

Generating Agreement in Computer-Mediated Groups

2001· article· en· W2162419628 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

VenueSmall Group Research · 2001
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsVotingAsynchronous communicationComputer scienceKey (lock)Collaborative softwareLinkage (software)AgreementInformation exchangeAnonymityGroup (periodic table)Computer-mediated communicationPsychologySocial psychologyKnowledge managementWorld Wide WebComputer securityComputer networkPolitical scienceThe Internet

Abstract

fetched live from OpenAlex

Agreement is an important social outcome often poorly handled by computer-mediated groups, presumably because the computer cannot transmit the necessary rich information. A recently proposed cognitive model suggests richness is not the key to social agreement and that group agreement can be generated by the exchange of anonymous, lean text information across a computer network. This experiment investigates this theory. Self-chosen groups of 5 completed three answer rounds on limited choice problems while exchanging a few characters of position information. These asynchronous, anonymous computer-mediated groups generated agreement without any rich information exchange. The key software design criteria for enacting agreement is proposed to be not richness but dynamic many-to-many linkage. The resulting “electronic voting” may be as different from traditional voting as e-mail is from traditional mail. It may also imply a new generation of groupware that recognizes social influence.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.108
GPT teacher head0.387
Teacher spread0.278 · 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