Challenges to the veracity and the international comparability of Russian homicide statistics
Bibliographic record
Abstract
Homicide statistics are often seen as the most reliable and comparable indicator of violent deaths around the world. However, the analysis of Russian homicide statistics challenges this understanding and suggests that international comparisons of homicide levels can be hazardous. Drawing on an institutionalist perspective on crime statistics, official crime-based homicide statistics in Russia are approached as a social construct, a performance indicator and a tool of governance. The paper discusses several incentives to misrepresent official homicide data in contemporary Russia, including politicization of homicide statistics as a legacy of the Soviet’ era’s falsified crime statistics and the role of policing. Mainly, the paper identifies and describes the exact legal, statistical and country-specific substantive mechanisms that allow homicide statistics to be distorted in Russia. By considering legal mechanisms alone, the more accurate homicide rate may be at least 1.6 times higher than that reported in the United Nations Office on Drugs and Crime Global Study on Homicide 2013.
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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 | 0.131 | 0.285 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".