What is Russia’s real homicide rate? Statistical reconstruction and the ‘decivilizing process’
Bibliographic record
Abstract
This article examines a paradox that relates to the issue of homicide in Russia. On the one hand, official police statistics demonstrate a rapid decline in the homicide rate in Russia in the 2000s, which is consistent with the stable economic growth (in particular after the financial crisis of 1998) and a stable political environment during the presidency of Vladimir Putin. On the other hand, other conditions and processes (e.g. rampant corruption, predatory policing, political repressions, state violence against businesses, rising xenophobia and apathy) point to what Norbert Elias terms a ‘decivilizing process’, which is expected to be associated with a less precipitous decline in homicide or stable homicide rate in this period. In fact, newly available homicide estimates suggest that the homicide rate was higher than and did not decline at a pace suggested by the official police and mortality sources in the 2000s. Hence, this article has two main objectives. First, it discusses issues around homicide statistics in Russia and argues that the newly available homicide estimates represent the more accurate statistics. Second, it explores decivilizing process theory as a potential framework for explaining a high and steady homicide rate in Russia in the 2000s.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".