Open Source Systems Engineering Guides, Deployment Packages and Support Tools for Very Small Enterprises ‐‐ A Case Study
Bibliographic record
Abstract
Abstract Very small entities (VSEs) play an increasingly important role in the global economy. The products they develop are often integrated into products made by larger enterprises. The “Big League” clients, furthermore, demand of the VSEs that they assume a much broader role, spanning the entire development life‐cycle of the product instead of being limited to a “build‐to‐print” approach. To address this new reality, to exploit the lean and efficient nature of VSEs and to adapt to their typical budget and resource constraints, the ISO/IEC 29110 systems engineering standards, management and engineering guides were developed from ISO/IEC/IEEE 15288. In addition, and by design, the standard is supported by Deployment Packages, software tools and training kits. The INCOSE VSE working group developed the deployment packages. A deployment package is a set of artefacts designed to facilitate the implementation of a standard or a set of practices in a VSE. In tune with the need for low cost and flexibility, Open Source software tools are emerging to complete “Big League” development life‐cycle toolsets, which are often out‐of‐reach to VSEs. Finally, to make the deployment of the standard possible in the VSE, training packages, supported by relevant pilot projects help VSE personnel learn how to apply all of the above.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".