Book Review: <em>The Role of Civil Society in Transitional Justice: The Case of Russia</em>
Bibliographic record
Abstract
This book review evaluates Selbi Durdiyeva's The Role of Civil Society in Transitional Justice: The Case of Russia as a vital contribution to genocide prevention scholarship, which is particularly timely given Russia's mass atrocity crimes in Ukraine and their connection to domestic repression. The book's theoretical innovation in challenging state-centric transitional justice paradigms through rigorous analysis of Memorial NGO, Orthodox Church activities, and revisionist groups across three decades of post-Soviet Russia is critical for the transitional justice field. It is worth highlighting Durdiyeva's unique postcolonial perspective as someone from a former Soviet territory studying the metropole, and her demonstration that civil society can substitute for absent government mechanisms rather than merely filling gaps. However, it is important to note the limitations including the study's Russia-specific focus limiting generalizability, insufficient victim perspectives, and limited assessment of resource efficiency compared to potential state mechanisms. Despite these constraints, the book offers indispensable insights for scholars working in authoritarian contexts where traditional accountability mechanisms fail, though comparative analysis with other post-Soviet states could strengthen its broader applicability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".