Policing following political and social transitions: Russia, Brazil, and China compared
Bibliographic record
Abstract
This is a comparative analysis of policing in three countries that have experienced a major political or social transition, Russia, Brazil, and China. We consider two related questions: (1) how has transition in each country affected the deployment of the police against regime opponents (which we term “repression”)? And (2) how has the transition affected other police misconduct that also victimizes citizens but is not directly ordered by the regime (“abuse”)? As expected, authoritarian regimes are more likely to perpetrate severe repression. However, the most repressive authoritarian regimes such as China may also contain oversight institutions that limit police abuse. We also assess the relative importance of both transitional outcomes and processes in post-transition policing evolution, arguing that the “abusiveness” of contemporary Brazilian police reflects the failure to create oversight mechanisms during the transition, and that the increasing “repressiveness” of Chinese police reflects a conscious effort by the Chinese Communist Party to reinforce the police in an era of economic liberalization. In contrast, Russian police are both significantly abusive and repressive, although less systematically “repressive” than Chinese police, and less “abusive” (or at least violent) than Brazilian police. Also, abuse and repression are less distinct in Russia than in the other cases. These results reflect the initial processes of decay and fragmentation, and subsequent partial recovery and recentralization, which Russian police have experienced since the Soviet collapse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".