Alcohol Control Policies and Alcohol-Related Mortality in Russia: Reply to Razvodovsky and Nemtsov
Bibliographic record
Abstract
We are happy to have our article (Khaltourina and Korotayev, 2015) reviewed by Prof. Nemtsov, whose work on alcohol-related mortality in Russia greatly improved our understanding of the problem, as well as by Prof. Razvodovsky, whose work provides important insights on alcohol situation in Belarus (Nemtsov and Razvodovsky, 2016). The effect of policy measures on alcohol mortality in Russia is a topic hard to research, because in this country alcohol is regulated predominantly at the national level, unlike in such countries as the USA, Canada and Australia where states and provinces have a mandate to develop their own laws, which allows for cross-sectional analysis of the policy effects. We only have one-time series data set without regional policy variation in Russia. There is also a problem of high unrecorded production and sales. Additionally, not all regulation documents are available for the public. Therefore, in our article, we have qualified our conclusions as interpretations and hypotheses. We are happy to discuss alternative explanations of the alcohol mortality dynamics in Russia, as long as they are well documented and substantiated, with ‘serious scientific proofs’, as Razvodovsky and Nemtsov (2016) call it.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".