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4.3.3 Capability Engineering within Canadian Defence: Experimentation and Lessons Learned

2008· article· en· W2069298775 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueINCOSE International Symposium · 2008
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsProcess (computing)DivestmentEngineering managementEngineeringWork (physics)Systems engineeringFocus (optics)Process managementComputer scienceManagement scienceBusinessMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This paper summarizes the results of an experimental evaluation conducted to assess the Capability Engineering approach developed for the Canadian Forces/Department of National Defence to facilitate decision‐making on strategic investments and divestments. This work was performed as part of the Collaborative Capability Definition, Engineering and Management Technology Demonstration Project (CapDEM TDP). Based on the systems engineering paradigm, the approach is articulated around three axes: People, Process and Materiel. The focus of this paper is on the lessons learned from Exercise Gamma, the last of three validation exercises which are part of an evaluation strategy attempting to evolve Capability Engineering from theory into practice. Lessons learned have helped to identify specific improvements as well as critical success factors for the implementation and application of Capability Engineering.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.511

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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