Danger from above? A quantitative study of perceptions of hazards from falling rockets in the Altai region of Siberia
Bibliographic record
Abstract
Russian rocket launch sites are inland, unlike their European and American equivalents. Those living near to fallout zones from the Baikonur cosmodrome have expressed concern about apparent high levels of psychological ill health, which they attribute to the launches, linking it to either fear of falling debris or contamination by fuel residues. The aim of the research study on which this article is based was to quantify and explain the reported ill health, relating it to exposure to, concern about, and information on the launches. Drawing on literature on social amplification of risk, a quantitative survey was conducted among 1111 adults living in three areas around the fallout area. Psychological symptoms were measured on the SCL-90 scale, with mental health assessed using the GHQ-20. Relationships were assessed using regression and path analysis. The main findings were that those people living closer to the fallout area were no more likely to have symptoms but were more concerned about launches. Prompted concern was associated with distress, assessed by SCL-90 scores (but not consistently with GHQ-20) and seemed to amplify the association between other adverse perceptions of life and symptoms. There is a high level of distress in this region but it is not obviously associated with exposure to launches. In contrast, the existing process of communicating information appears to increase concerns and thus distress.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".