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Version 0.75 of the Proposed INCOSE Competency Framework

2017· article· en· W2751624834 on OpenAlexaff
Don Gelosh, Mimi Heisey, John Snoderly, Ken Nidiffer

Bibliographic record

VenueINCOSE International Symposium · 2017
Typearticle
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCompetence (human resources)Core competencySection (typography)Computer sciencePublicationEngineering managementKnowledge managementEngineering ethicsEngineeringPsychologyPolitical scienceManagementOperating systemSocial psychology

Abstract

fetched live from OpenAlex

Abstract The next evolution of the INCOSE Competency Framework is Version 0.75. This paper describes some of the major sections of the Version 0.75 document. One of the major sections describes the updated competency framework structure which has evolved into competence groups and core competence areas. Another section is a draft guide to role definition that describes the typical roles that systems engineers may assume. One major section discusses some use cases with a detailed look at recruitment, education program improvement and professional development. The document includes a section that explains how to tailor the competency framework to suit using organizations. This paper concludes by exploring all the remaining coordination activities and other actions required to successfully develop and publish Version 1.0 of the new INCOSE Competency Framework.

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)0.040
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.044
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0020.003
Scholarly communication0.0100.007
Open science0.0030.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.009

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 head0.269
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations8
Published2017
Admission routes1
Has abstractyes

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