A New Vision for Development Assurance Guidance
Bibliographic record
Abstract
In the last several years, technical advances and regulatory pressures have motivated the need for flexible, simple, and performance-based solutions for conducting development assurance in support of a system safety assessment process. Additionally, the affected design space for commercial vehicles has been growing beyond the conventional regulations for airplanes, rotorcraft, engines, and propellers, addressed by current Aerospace Recommended Practices (ARPs). This space is beginning to include commercial technologies such as unmanned aerial systems, multi-stage spacecraft systems, and road-able aircraft. These developing areas are each accompanied with their own development assurance expectations in support of their safety criteria. Concurrently, the industry and regulators are working to simplify guidance for system safety and development assurance, which has been foundational in the aircraft industry for decades. From the 14 Code of Federal Regulations (CFR) Part 23 reorganization to the Streamlining Assurance Process Workshops, the existing regulations and guidance are under pressure to adjust to new expectations within these technical areas. There are several challenges with the existing development assurance guidance which complicates both its application to these new technical areas and its alignment with these streamlining expectations. These challenges include application to programs with a combination of new and legacy systems and ARP alignment with regulations and other guidance. Meeting these challenges requires a shift from overly prescriptive and complex guidance to a proposal which provide clear expectations towards planning for, assessing, and achieving objectives that align with regulations. This work seeks to capture the fundamental objectives of development assurance, to promote balanced application and the flexibility of methods that may be applied to them in a changing environment. It proposes a single Aerospace Standards which will address these issues, while still permitting the continued use of ARPs as guidance.
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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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.028 | 0.044 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".