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Designing High Performance Virtual Teams

2005· book-chapter· en· W167504809 on OpenAlexaff
Ian K. Wong, D. Sandy Staples

Bibliographic record

VenueIGI Global eBooks · 2005
Typebook-chapter
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsTeamworkInterdependenceVirtual teamThe InternetWork (physics)Knowledge managementEngineeringComputer scienceWorld Wide WebManagementSociology

Abstract

fetched live from OpenAlex

In the past several decades, we have seen tremendous advancements in the development of communication technology. Since the invention of the Internet in 1969, there has been rapid development of Internet-based communication tools and technologies. This technology has revolutionized business practices by offering another important and effective channel for communication (Foo & Lim, 1997), and has allowed people to work on projects irrespective of their physical location. One resulting business practice that has been adopted in recent years is virtual teamwork. Virtual teams are groups of individuals who work at interdependent tasks, who share responsibility for outcomes, and who work together from different locations. Recently, the use of teams as fundamental building blocks in organizations is increasing, as is the use of virtual teamwork (Furst, Blackburn & Rosen, 1999). This article identifies the characteristics of high performing virtual 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2005
Admission routes1
Has abstractyes

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