Introduction: Race, Illiberalism, Central Europe
Bibliographic record
Abstract
Who would have thought? A village boy noted for his soccer prowess. Football is his life, but he's not good enough to become a pro. Makes it to a good university, but never breaks into the circle of chic students from better-connected families. Maybe he doesn't even want to. Starts to dabble in politics. Speaks at the reburial of a communist leader who rebelled against the communists. He has no patience for reform communism; to him it's an illness that must be expounded. The cure is the party, Fidesz, that he founds mainly with other friends with similar, relatively unprivileged, backgrounds. Wins a scholarship to Oxford. Feels out of place, quits, returns to Budapest where the communist regime is busy dismantling itself. After it does, Fidesz wins one election, then loses, then comes back reborn as a movement of ‘illiberal democrats’. ‘Liberal’ and ‘globalist’ become the names of the enemy, a vast conspiracy of money men pulling the strings behind the scenes, ruling the world, Washington, Brussels, and Budapest. Some hear anti-Semitic dog-whistles, but he denies that he means ‘the Jews’. Dines with Silvio Berlusconi in Italy, sits around the Oval Office fireplace with Donald Trump. When Trump falls, the two stay in touch. In his village, Felcsút, folks are proud of Viktor Orbán. They love the world-class football stadium he built there. The Puskás Arena seats 3,500, almost double the population of the community. Amenities include a fine hotel reported to have cost nearly $35 million of taxpayers’ money. Lőrinc Mészáros, one of Orbán's childhood buddies, became the mayor of the village as soon as Orbán was re-elected prime minister in 2010. With Orbán's protection, this humble gas installer's wealth rose spectacularly, until Forbes named him Hungary's richest man, with a finger in every pie, from construction and hotels to agriculture. In 2017 Lőrinc confessed, ‘My fortune is due to three factors: God, luck, and Viktor Orbán’. Many people think he is Viktor Orbán, the real owner of the assets. Not since the fiery nineteenth-century revolutionary, Lajos Kossuth (half Slovak, half German by descent, now largely forgotten outside of Hungary), or the composer Franz Liszt (who grew up and lived outside the country), has a Hungarian been as much the talk of the world as Viktor Orbán.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.008 |
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".