The X-Culture Handbook of Collaboration and Problem Solving in Global Virtual Teams
Bibliographic record
Abstract
According to a 2018 survey, 89 percent of "white-collar" workers at least occasionally work as members of global virtual teams. The percentage has likely increased during the COVID-19 pandemic as bans on international travel and the shifts to telework prompted more online collaboration. Collaboration among people from different countries, cultures, organizations, and institutional environments presents numerous advantages; the diversity of perspectives and knowledge pools greatly enhances the team's creativity and decision-making. However, such workgroups often have to deal with time-zone differences, limited in-person contact substituted by communciation online, and the differences stemming from culture and institutional diversity, which presents challenges not experienced by traditional collocated teams. Based on a wealth of research and personal experiences, contributors to The X-Culture Handbook of Collaboration and Problem Solving in Global Virtual Teams review known challenges and recommend evidence-based best practices for working in global virtual teams. The book provides practical advice not only to members of global virtual teams, but also for team managers, coaches, counselors, and educators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".