How Safe is Safe Enough? Acceptable Safety Criteria From an Engineering and Legal Perspective
Bibliographic record
Abstract
Manufacturers have a vested interest in the safety of their customers, and in protecting their reputation for producing safe products. An additional incentive to produce safe products is avoiding liability when their product is involved in an accident or mishap that results in personal injury and/or property damage. While it is often said that one must never compromise on safety, the fact remains that any product must necessarily be a balance between the level of safety desired and the cost and performance impact of achieving that level of safety. The product manufacturer must make a determination: Is this product (or technology) acceptably safe within the context of current consumer expectations as well as the legal/regulatory framework? Is the residual risk tolerable? This paper presents a methodology to address those questions by reviewing the publicly available information of a recent automotive product liability case, and evaluating whether the product design met current legal and safety engineering best practices.
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 | 0.027 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| 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".