The Politics of Exclusion and Retribution in the Hungarian Film Industry, 1929-1947
Bibliographic record
Abstract
This dissertation examines how the interwar and postwar governments in Hungary politicized and shaped the film industry to do their bidding and how filmmakers, actors and actresses reacted to those political pressures. The interwar conservative government of Miklós Horthy set out to mold the film industry to suit its ideals and ideas, while, at the same time, seeking to limit the number of Jews in the professions. This dissertation investigates the resulting conflicted political forces that brought about the creation of the Theatre and Film Arts Chamber. It examines the impact of the chamber, specifically how those working in the film industry were affected by these laws and new measures. The archival files of the postwar certification committees provide significant historical insight into the leadership and antisemitic narrative of the entertainment industry during the interwar era. In particular, these files illuminate the motivations of the individuals leading the Theatre and Film Arts Chamber. The certification committees established to investigate the wartime activities of those involved in the film industry played a significant role in creating a positive postwar identity. In 1945, this identity grew out of the belief that Hungary had been a victim of Nazi aggression. The postwar system of retribution sought to smooth the transition, and to salvage the once thriving film industry. After the Communist Party consolidated its hold on the government in May 1949, the regime recruited and rehabilitated famous Hungarian actors from the interwar era. The alleged crimes of these actors and actresses and their subsequent postwar convictions were no longer an issue of importance. The political trials that unfolded in the late 1940s served the position and power of the new elites more than anything else. By the early 1950s, the Cold War took precedence over retribution following the Second World War and the outcome of these trials seemed irrelevant. This dissertation notably adds to the research and discussion of how to shape, and for what purpose, a nation’s memory of the war and postwar years. This question is still being formed and framed in Hungary today, as it is in many European countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".