Bibliographic record
Abstract
“How afraid sometimes I was of my freedom, how afraid most of us are of freedom, how hard it is to maintain the sovereignty of our own judgment. To see the world as it is, not as we would wish it to be, how truly we must all struggle to be free men and women in a world saturated with manipulation and lies. Yet to call ourselves free and to actually deserve it is the prize that matters most in a life.” – Michael Ignatieff During the twilight hours of Soviet influence over Eastern Central Europe, a young Viktor Orban called for respecting "open society" ideals, like democracy, for Hungarians. However, since taking power in 2010, Orban has become a political juggernaut by championing illiberalism and openly criticizing the ideas he advocated for in his youth, for which he may still win the 2026 election. This illiberal turn is particularly represented by his attacks on universities in Hungary, notably the Central European University (CEU). The literature on this topic is vast, though most papers fail to properly analyze the theoretical arguments for an "open society" versus a "closed society" in Hungary. As a result, this paper's central question is why Orban has attacked "open society" values and its leading representative, the university, and what messaging he uses for that success. To do so, I will analyze primary sources of writings by influential philosophers on the concept of "open" and "closed" societies and secondary sources that demonstrate how Orban has mobilized the "revolution of resentment" – the economic and cultural changes of Hungarian society – to mobilize the Hungarians against the CEU, causing the CEU to leave Budapest. "Closed society" ideals are on the rise. The case study of Hungary is an essential way to understand the rationale behind that rise and to demonstrate how "open society" values can fight back.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.081 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".