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Record W2121719018 · doi:10.1177/009365001028003004

Impression Formation in Computer-Mediated Communication Revisited

2001· article· en· W2121719018 on OpenAlexaff
Jeffrey T. Hancock, Philip J. Dunham

Bibliographic record

VenueCommunication Research · 2001
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsDalhousie University
Fundersnot available
KeywordsImpression formationPsychologyConversationImpressionNeuroticismPersonalitySocial psychologyContext (archaeology)AttributionImpression managementBig Five personality traitsFace (sociological concept)Computer-mediated communicationCognitionTraitConscientiousnessExtraversion and introversionPrecedentCognitive psychologySocial perceptionComputer sciencePerceptionLinguisticsCommunication

Abstract

fetched live from OpenAlex

Following either a text-based, synchronous computer-mediated conversation (CMC) or a face-to-face dyadic interaction, 80 participants rated their partners' personality profile. Impressions were assessed in terms of both their breadth (the comprehensiveness of the impression) and intensity (the magnitude of the attributions). Results indicated that impressions formed in the CMC environment were less detailed but more intense than those formed face-to-face. These data provide support for theories that, in addition to acknowledging the unique constraints and characteristics of CMC, consider the cognitive strategies and heuristics involved in the impression formation process. The differential impact of a text-based medium on trait-specific impressions (e.g., extraversion, neuroticism) is also discussed in the context of a cross-modal approach to impression formation.

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)0.006
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.124
GPT teacher head0.458
Teacher spread0.334 · 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

Citations350
Published2001
Admission routes1
Has abstractyes

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