Socio-demographic factors of industrial injuries of railway workers
Bibliographic record
Abstract
The article identifies socio-demographic factors of industrial injuries sustained by railway sector workers, as well as the practice of applying the Vision Zero concept aimed at improving road safety. In the process of writing this scientific article, the following methods were used: integration of research data produced in Canada and France, analysis of statistical data of the studies under consideration; comparative analysis of socio-demographic factors of occupational injuries identified in Canada, France, London and Malaysia, comparison with data for Russia; systematic analysis of the practices of applying the Vision Zero concept in the railway transport sector. The results obtained allow us to identify socio-demographic factors that have the strongest impact on occupational injuries in the field of railway transport. Apart from this, the result of this research allows management structures to be able to neutralize or minimize the effect of the identified factors and reduce the number of occupational injuries, including fatal ones. The identified trends make a huge contribution to improving the safety of the railway industry and its development generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".