The Definition and Assessment of a Safety Argument
Bibliographic record
Abstract
That safety cases are gaining prominence in safety regimes and regulations is a claim that, nowadays, may go more or less unchallenged. In brief, a safety case intends to make an explicit and compelling case that a system under consideration is safe for its intended use. When understood in this sense, the notion of a safety argument becomes one of the key elements of a properly formulated safety case. Herein, in what may be seen as work in progress, we comment on some preliminary thoughts regarding the challenges one must face in order to provide an adequate and sensible definition of what would count as being a safety argument. We contend that, without such a definition, the assessment of a safety argument is well-nigh impossible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.031 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 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".