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3.5.2 Virtual Collaboration, e‐SE, and Team Sports Metaphors –Opportunities to Innovate, Integrate, and Invigorate

2001· article· en· W2047502166 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINCOSE International Symposium · 2001
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsVirtual teamVariety (cybernetics)Theme (computing)Process (computing)Knowledge managementDestiny (ISS module)Computer scienceVirtual realityEngineeringEngineering managementWorld Wide WebProcess managementHuman–computer interaction

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.270
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it