Occupational accidents in Russia and the Russian Arctic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it