Bibliographic record
Abstract
Virtual teams are now being used by many organizations to enhance the productivity of their employees and to bring together a diversity of skills and resources (Gignac, 2005; Majchrzak, Malhotra, Stamps, & Lipnack, 2004), and it has been suggested that this will become the normal way of working in teams in the near future (Jones, Oyund, & Pace, 2005). Virtual teams are groups of individuals who work together from different locations (i.e., are geographically dispersed), work at interdependent tasks, share responsibilities for outcomes, and rely on technology for much of their communication (Cohen & Gibson, 2003). While the use of virtual teams is more common in today’s organization, working in these teams is more complex and challenging than working in traditional, collocated teams (Dewar, 2006), and success rates in virtual teams are low (Goodbody, 2005). This article suggests best practices that organizations and virtual team members can follow to help their virtual teams reach their full potential. In this article, virtual team best practices are identified from three perspectives: organizational best practices, team leadership best practices, and team member best practices. Ideas for best practices were identified from three sources: six case studies of actual virtual teams (Staples, Wong, & Cameron, 2004); the existing literature on virtual teams; and the existing literature on traditional (i.e., collocated) teams and telecommuting (i.e., research on virtual work at the individual level).
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".