Bibliographic record
Abstract
An important literature on Russian civil society discusses its evolution, challenges, and prospects under Vladimir Putin. In particular, scholars show how the regime skillfully uses a mixture of coercive and channeling strategies to direct civil society into the ‘right path’, namely in the service of the regime. Perhaps the most glaring example of channeling strategies is the direct creation of CSOs from above, such as pro-regime youth groups. These groups are mean to orient public participation into accepted limits fixed by the state, often mimicking and duplicating grassroot organizations. But to what extent have they been effective in creating loyalty for the regime? In this paper, I focus on the little success that one of the most famous pro-regime youth groups, Nashi (Ours), paradoxically achieved in channeling civil society. While Nashi undeniably brought important benefits to some participants at the individual level, its effects at the societal level are significantly more limited. This is because, I argue, Nashi’s fate, just like many other state-projects, depended primarily on internal competition among self-interested elites. Instead of representing a coherent state strategy toward the youth and civil society, Nashi was mirroring the influence of power-maximizing individuals. The arguments of this paper are drawn from participant observations and from interviews with (then) current and former Nashi activists, as well as with other civil society experts
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".