Culture Wars as Property Struggles: The Hungarian Academy of Arts in Post-1989 Hungary
Bibliographic record
Abstract
Abstract While property is a common analytical category in political economy, it is often omitted from cultural analysis. To bridge this gap, this article examines the Orbán regime’s cultural flagship institution, the Magyar Művészeti Akadémia [Hungarian Academy of Arts, HAA] from a property perspective. By putting the 1992‒2011 trajectory of the Academy under a spotlight, a rarely examined prehistory of the current Orbán regime comes to the fore. By deploying historical and social sciences literature alongside archival and interview-based primary research, it is here argued that the property perspective can transcend the “culture wars” explanations of postsocialist cultural polarization by highlighting the material roots of such conflicts. The case of the HAA serves to demonstrate that its cultural clashes were property struggles to obtain properties from the postsocialist state. In this quest, the Academy formed an uneven alliance with the national-protectionist elite bloc. By demonstrating how the HAA was bound to the competition of postsocialist elite blocs, its cyclical politicization can be best understood from the angle of the rapidly transforming property regime in post-1989 Hungary. In conclusion, the article proposes the postsocialist property regime of culture as a framework to analyze cultural and property clashes in tandem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".