3.5.2 Virtual Collaboration, e‐SE, and Team Sports Metaphors –Opportunities to Innovate, Integrate, and Invigorate
Bibliographic record
Abstract
Abstract Participation in virtual teams performing systems engineering‐related activities is a part of our collective destiny. While many of the functions of systems engineering (SE) are already difficult, they may be even more challenging in virtual team settings. As we transition to an electronic‐systems engineering (e‐SE), our success will depend in part on whether the environment, training, culture, infrastructure, and processes provide the appropriate support for various types of virtual collaboration that will occur within a project or enterprise. This paper characterizes several models of collaboration and virtual collaboration – some of them based on team sports metaphors. We identify and describe different kinds of web‐based support that may be advantageous to these variations on the collaboration theme. In the process, we identify a variety of opportunities for innovation and integration – all with the aspiration of helping to invigorate SE‐related virtual team processes and activities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".