Occupational accidents in Russia and the Russian Arctic
Bibliographic record
Abstract
BACKGROUND: According to official statistics, the rate of occupational accidents (OAs) and fatal injuries in Russia decreased about 5-fold and 2-fold, respectively, from 1975 to 2010, but working conditions during this period had the opposite trend; for example, the number of people who work in unfavourable and hazardous conditions (particularly since 1991) has increased significantly. METHODS: This review summarises the results of a search of the relevant peer-reviewed literature published in Russia and official statistics on OAs and occupational safety in Russia and the Russian Arctic in 1980-2010. RESULTS: The occupational safety system in Russia has severely deteriorated in the last 2 decades, with legislators tending to promote the interests of industry and business, resulting in the neglect of occupational safety and violation of workers' rights. The majority of workers are employed in conditions that do not meet rules of safety and hygiene. More than 60% of OAs can be attributed to management practices--violation of safety regulations, poor organisation of work, deficiency of certified occupational safety specialists and inadequate personnel training. Research aimed at improving occupational safety and health is underfunded. There is evidence of widespread under-reporting of OAs, including fatal accidents. Three federal agencies are responsible for OAs recording; their data differ from each other as they use different methodologies. The rate of fatal OAs in Russia was 3-6 times higher than in Scandinavian countries and about 2 times higher compared to United States and Canada in 2001. In some Russian Arctic regions OAs levels are much higher. CONCLUSIONS: Urgent improvement of occupational health and safety across Russia, especially in the Arctic regions, is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".