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Agility in the Future of Systems Engineering (FuSE) ‐ A Roadmap of Foundational Concepts

2021· article· en· W3199906350 on OpenAlexaff
Keith Willett, Rick Dove, Alan Chudnow, Rusty Eckman, Larri Ann Rosser, Jennifer S. Stevens, Robin Yeman, Mike Yokell

Bibliographic record

VenueINCOSE International Symposium · 2021
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsAgile software developmentProcess (computing)Fuse (electrical)EngineeringSystems engineeringEngineering managementSet (abstract data type)System of systems engineeringProcess managementComputer scienceSoftware engineeringSystems designElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The Future of Systems Engineering (FuSE) is an INCOSE led multi‐organization collaborative activity focusing on many initiatives to identify and shape the future of systems engineering. The FuSE Agility collaboration identifies and elaborates a roadmap for an initial set of foundational concepts to further the integration of agility into the systems engineering lifecycle. This paper identifies four objectives for agility integration in people, process, technology, and environment and aligns nine foundational concepts to advance thinking and practice in agile‐systems engineering (solutions) and agile systems‐engineering (process).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0020.009
Scholarly communication0.0110.012
Open science0.0020.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.279
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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