A guide to global virtual teaming
Bibliographic record
Abstract
Purpose The purpose of this article is to share with readers details of this consortium's multicultural virtual teaming project implementation and the lessons learned from experiences of the participating students and professors. Design/methodology/approach To establish a preliminary relationship, virtual student teams exchange e‐mail messages with team mates at participating universities that provide introductions for each member of the team. Each team member uses these individual introductions to write a brief paper that introduces all team mates. Next, the students virtually interview one another to obtain answers to culture‐specific questions for each culture that is represented on the team. In some courses, this information is analysed using Hofstede's four dimensions of culture: power distance, individualism versus collectivism, uncertainty avoidance, and masculinity versus femininity. Findings Based on participants' experiences in these virtual teaming projects, the following recommendations are presented: emphasise relationship building; solicit widespread input for planning; and balance individual control with shared objectives. Originality/value These cultural virtual teaming projects proved to be valuable learning experiences for both the students and faculty who were involved.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.084 |
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".