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Record W4312249810 · doi:10.46692/9781529213621.001

Introduction: Race, Illiberalism, Central Europe

2022· other· en· W4312249810 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRace (biology)GeographyBiologyPaleontology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.068
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0680.008

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.

Opus teacher head0.017
GPT teacher head0.259
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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