Corporate Manslaughter Legislation, Public Policy and the Legal Response to Workplace Accidents
Bibliographic record
Abstract
Abstract: Recent rail accidents in the UK have focussed public attention on the role that companies play in the causes of incidents and accidents. Partly in response, the Westminster parliament has published proposals to change the legislation on corporate manslaughter. Previous incidents have had a similar impact in other countries. For example, the 2006 mining accident in Sago, West Virginia has prompted calls to recognise the responsibility that executive officers share in creating the conditions in which adverse events are likely to occur. There are strong parallels between this accident and the 1992 Westray mining disaster, which motivated significant changes in the Canadian jurisdiction. Similarly, the Longford explosion in Victoria prompted further reviews in Australia. The following pages provide an overview of the issues surrounding legislation for corporate manslaughter. The review focuses on existing provisions in Canada and Australia as well as recent proposals in England, Wales and Scotland. It forms part of a wider comparative analysis that is intended to help the formation of public policy over the reform of corporate manslaughter legislation.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".