Conceptualizing and measuring the virtuality of teams
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.
Bibliographic record
Abstract
Abstract Virtual teams (VTs) are teams whose members do not share a common workspace all of the time, and must therefore collaborate using communication and collaboration tools such as email, videoconferencing, etc. Although the body of research on VTs is quickly expanding, to date, the field has yet to produce a comprehensive and coherent foundation upon which future research can be based, and empirical findings based on a substantive sample of real VTs remain limited at this time. This study fills a void in the VT literature with respect to defining and operationalizing the construct of degree of virtuality, and responds to calls for research that studies ongoing VTs, under real conditions. Data were collected from 30 VTs working in a Canadian technology‐based organization. Degree of virtuality was defined to include three dimensions: the proportion of work time that the VT members spend working apart (team time worked virtually), the proportion of the team's members who work virtually (member virtuality) and the degree of separation of the team's members (distance virtuality). The VTs in this study were found to have varying degrees of virtuality, and although the three dimensions were not highly intercorrelated, all were found to be significantly correlated to variables that have been previously linked to VT effectiveness. The correlations were all in the expected direction (negative), indicating that higher degrees of virtuality are associated with perceived decreases in the quality of team interactions and performance. The results of this research would suggest that the more that teams move away from the proximate form, the more the traditional measures of team effectiveness are negatively impacted.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it