Bibliographic record
Abstract
In this paper, I argue that the current Hungarian and Polish governments—Fidesz and the Law and Justice (PiS) parties, respectively—engage in historical revisionism to legitimize their illiberal regimes. They act as “mnemonic warriors” by mobilizing, or weaponizing, history for their political gain. They rebuke international criticism of their democratic backsliding, erosion of the rule of law, and media censorship by emphasizing their role as defenders of the nation. Specifically, Viktor Orbán and Jarosław Kaczyński seek legitimacy by grounding their historical interpretations in wartime resistance movements. They continually praise these wartime figures for fighting valiantly to defend their nations from foreign attacks and try to position themselves as their successors, striving to extend these historical narratives of heroism and struggle to their current fight against European Union elites, who criticize their democratic backsliding and illiberalism. In this paper, I will discuss how Orbán and Kaczyński try to revise the official and popular memory of their nations’ experiences in both the Second World War and Revolutions of 1989. I analyze specific monuments, museums, and laws implemented by each regime and compare and contrast their historical revisionism efforts—namely, victimization in Hungary and heroism in Poland—within their current political context to show how they mobilize their historical narratives in their fight against EU elites.
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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 | 0.002 | 0.004 |
| 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 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".