Building an inductive theory of collaboration in virtual teams: an adapted grounded theory approach
Bibliographic record
Abstract
We outline how the grounded theory methodology (Strauss and Corbin, 1994 version) was adapted to develop a theory of collaboration in virtual teams. Specifically, we studied virtual teams composed of students from a US and a Canadian university engaged in 14 week long systems development projects. We analyzed data using adapted versions of open coding, axial coding and selective coding. Based on our theoretical sensitivity, we also developed a meta-theoretical framework through a synthesis of the data we interacted with, the symbolic interactionist perspective, and structuration theory. We used this framework as an alternative to the "paradigm model" during selective coding of data. This paper makes two important contributions: methodologically, it can serve as a guide for researchers interested in using the grounded theory approach; and substantively, it presents a holistic and processual understanding of virtual teams that researchers in this area have called for.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".