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Record W2052697227 · doi:10.4018/jec.2013040101

Virtual Teams Demystified

2013· article· en· W2052697227 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

VenueInternational Journal of e-Collaboration · 2013
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsVirtual teamComputer sciencePoint (geometry)Knowledge managementVirtual realityManagement scienceHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Virtual teams have been researched intensely in the last ten years and there is a growing body of literature on the topic. At this point, the authors need an integrative theory-driven framework through which they can conceptualize the notion of virtual teams and organize and make sense of prior research. This can help them better understand what drives virtual team dynamics and ultimately effectiveness and can guide future research on the topic. Drawing on models of team effectiveness and emergent processes and states, the authors developed a framework for understanding virtual team dynamics. They then use this framework to review and synthesize one hundred and twenty-one empirical studies of virtual teams published since 1990. The authors analyzed the direct and indirect antecedents of virtual team effectiveness and identify key gaps in both their knowledge of, and approach to studying, virtual teams. They outlined areas for future research and discuss, the implications for the authors’ paper for practice and for the study of virtual and traditional teams.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.312
Teacher spread0.304 · 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