Understanding industry learning behaviours in the context of safety: A railways perspective
Bibliographic record
Abstract
• Hazard of deterioration in railway safety is explored. • Industry learning behaviours in the safety context are investigated. • Failure to learn across jurisdictions in the rail industry is identified. • Barriers to advancing the safety culture are revealed. • Extending the breadth and depth of railway safety is suggested. From its inception, the railway industry has sought to develop a safety culture where hazards are identified and addressed and all parties commit to improving safety. However, efforts are often fragmented with limited understanding of cross-jurisdictional knowledge sharing, potentially leading to the under-researched hazard of deterioration in railway safety over time. The paper provides an overview of how the industry conducts safety knowledge retrieval, processing, and dissemination, as well as its relevance for learning behaviours and safety culture. The triangulation method is used to examine the learning behaviours of railway industries in the UK, the USA, Canada and Australia by co-reference analysis of railway accident reports, review of literature in other high-reliability organisations (HROs), and outcomes of stakeholder workshops and participant surveys. The results show the significance of a strong safety culture in promoting a safe environment underpinned by rigorous legislative frameworks and incorporating incentives to learn from historical accidents. Furthermore, failure to learn across jurisdictions may result in misapplied investment and misunderstandings in the prioritisation of resources for advancing railway safety. Several barriers to advancing the safety culture that lead to the potential deterioration of the safety culture have been investigated, including a lack of motivation for cross-jurisdiction learning, legislative framework restrictions, struggles in maintaining corporate memory and technological limitations. Another key conclusion is that learning across jurisdictions and over time fosters a proactive safety culture that anticipates and mitigates risks before they result in accidents.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".