Jury Trials for Violent Hate Crimes in Russia: Is Russian Justice Only for Ethnic Russians
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
The article examines issues of potential anti-victim jury bias in hate crime trials of skinheads in Russia. The study is based on the analysis of court transcripts and interviews with judges, prosecutors, defense attorneys, and victims' lawyers who participated in four high profile criminal cases. The cases selected for analysis resulted in scandalous acquittals, which raised many questions within the Russian society as to whether lay citizens can and should adjudicate hate crimes committed against members of ethnic and racial minority groups. The results of the study have revealed that the juries in these cases did not demonstrate any bias against ethnic and racial minority victims. On the contrary, it can be suggested that after hearing evidence presented to them, juries were left with a reasonable doubt regarding the guilt of the accused.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it