Incremental Assurance Through Eliminative Argumentation
Bibliographic record
Abstract
An assurance case for a critical system is valid for that system at a particular point in time, such as when the system is delivered to a certification authority for review. The argument is structured around evidence that exists at that point in time. However, modern assurance cases are rarely one-off exercises. More information might become available (e.g., field data) that could strengthen (or weaken) the validity of the case. This paper proposes the notion of incremental assurance wherein the assurance case structure includes both the currently available evidence and a plan for incrementally increasing confidence in the system as additional or higher quality evidence becomes available. Such evidence is needed to further reduce doubts engineers or reviewers might have. This paper formalizes the idea of incremental assurance through an argumentation pattern. The concept of incremental assurance is demonstrated by applying the pattern to part of a safety assurance case for an air traffic control system.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 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".