Bibliographic record
Abstract
In recent years, the anti-Putin content of Pussy Riot’s work has received sustained scholarly attention. The author argues, however, that it is not only in content but also in force – in operationality of noise, in its capacity to act and incite – that Pussy Riot engaged with Russia’s regimes of power. Once Pussy Riot emerged as a feminist collective performing noisy interventions in public spaces and mapping them onto cyberspace, their performances were described as non-musical and aesthetically unpleasant based on negative stereotyping of noise. This article takes the ambiguous feeling of the non-musical as a point of departure and explores how sound was integral to the formation of Pussy Riot’s identity as noisemaker in the face of the Kremlin’s noise-abatement campaign associated with the moral project of silence. It builds on a philosophical framework proposed by Michel Serres and the latest theoretical developments in sound and affect studies to examine the urgency of Pussy Riot’s work in the contemporary political climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.051 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".