How INCOSE's Certification Program has Evolved as a System of Systems
Bibliographic record
Abstract
Abstract The International Council on Systems Engineering (INCOSE) Certification program began as a subsystem of INCOSE, with only a few external entities involved. The majority of the required capabilities were carried out internally, such testing based on the INCOSE Systems Engineering Handbook. Many capabilities have since been outsourced to independent external agencies such as psychometricians and certificate providers to gain flexibility and simplify operations. As a result, the INCOSE certification program evolved from an INCOSE subsystem to a System of Systems (SoS) with component systems such as universities, exam providers, training providers, and local chapters. This paper discusses the characteristics and challenges of the INCOSE Certification program as a System of Systems, the type of a SoS that best suits the certification program, change management of the certification program, learnings from managing the certification program as a System of Systems, the System of Systems engineering application to the certification programs, and critical problems involved in the certification program's operation and management.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".