Taking a page from the law books: Considering evidence weight in evaluating assurance case confidence
Bibliographic record
Abstract
This brief report is a contribution to discussions of the notion of confidence in the context of assurance cases. In this work, we draw a parallel between the concepts of assurance case confidence and evidence weight in the legal domain, and explore the practical ramifications of this idea. We first establish what factors influence assurance case confidence, and propose a definition. Then, through a comparison with the legal domain (following the discussions of Jonathan Cohen, Keynes and Nance) we submit that confidence can be seen as composed of two distinct aspects, and we proceed to contend that it is beneficial to consider these aspects separately when performing an evaluation. One of the greatest advantages of doing so would be providing a separate measure for assurance case “ripeness” for review (to be used by assurance case developers, as well as regulators).
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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 | 0.198 | 0.740 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".