Contention, consensus, and memories of communism: Comparing Czech and Slovak memory politics in public spaces, 1993–2012
Bibliographic record
Abstract
Memorial spaces can reinforce consensus or deepen conflict over the past. Memories of communism in Europe are particularly fraught mnemonic landscapes. Although they experienced the communist regime as a single country, Czechs and Slovaks now manifest very different political memories in public spaces with different levels of contention surrounding them. Through a media analysis (1993–2012) of events surrounding the 12 memorials addressing the communist past in the capital cities of Prague and Bratislava, this study generates a theory of differing levels of contention between societies with similar ‘difficult’ pasts. The Czech case is characterized by official and unofficial actors, who are cooperative or noncooperative, presenting often competing versions of the past through an individualistic, human rights-focused mnemonic frame. Slovak memory politics are less contentious, dominated by official memory actors, and interpreted through religion and nationalism. The collective memory literature lacks a way to understand when contention is more or less expected over a problematic past. I propose that when official memory actors privilege an individualistic mnemonic frame, contention becomes likely through the interpretations of unofficial memory actors, while a more collectivistic frame results in less contentious memory politics. In other words, the variation in mnemonic frame helps to explain why unofficial actors sometimes contest official representations of the past and other times leave them unchallenged.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".