Torture to Their Ears, Music to Ours: Memory Regimes and the Ordering of Political Space
Bibliographic record
Abstract
How do societies navigate the symbolic and artistic heritage of troubled pasts? I build on Bernhard and Kubik’s (2014) theorization of official memory regimes to demonstrate how memory regimes govern the public mnemonic space beyond the official level. I trace such governance within what Bernhard and Kubik call a unified memory regime , in which official actors prefer not to fight battles in and around memory. I argue that unified memory regimes order, discipline, and govern not only the official but also the everyday spaces of judgment and affection. Posited on unity at the official level, these hegemonic frames of meaning-making relegate mnemonic tension to the societal level where discursive battles continue to take place. I further argue that unified memory regimes can open pitfalls for pluralists during moments of mnemonic contestation. Because pluralists acknowledge and agonistically deliberate on multiple interpretations of the past, they may attempt to discursively reconcile the emerging societal-level mnemonic fracture with the official unified memory regime. But this strategy can backfire, reinforcing the unified regime and disciplining the societal-level challenger through three discursive practices that I call the traps of consensus : semantic alignment, a syntax of disavowal, and the juxtaposition of “universal” and “particular” narratives about the past. Pluralists may be especially vulnerable to these traps when they face unified memory regimes in which the consensus narrative appears superficially pluralist (or “underspecified”) because it eschews normative judgments that distinguish between perpetrators and victims. I illustrate these dynamics through tracing the case of a contested soundscape in postcommunist Albania.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".