Bibliographic record
Abstract
Let us stay with football for a moment longer. I would like to begin the Conclusion as I have the Introduction, with an allusion to the best-known Central European illiberal leader's penchant for the game. The Hungarian journalist, Pál Dániel Rényi, wrote a whole book on it, claiming that Orbán's view of football was the same as his vision of politics. ‘Orbán has never been a fan in the traditional sense’, writes Rényi, but he knew well the emotional world of a fan. That sort of commitment is built not on rational pondering but on events, beliefs, heroes, and memories. Shared symbols, convictions, and goals connect to one another the team and its followers. Identification is unconditional, requires no explanation, and resists time. The team: it's eternal. And for the loyal fan, victory is not simply important; it's the only thing that counts. This was not Orbán's own world, as Rényi makes clear; it was the world he created for his fans. With this kind of mentality, every victory by the leader is cheered by his followers, with little thought given to how it benefits them . He plays the strings of their beliefs and memories; he offers a community of affect at a time of great dislocation. Orbán became a master at this political game, unlike any in Central Europe or even Europe as a whole. But the false consciousness that illiberalism creates is systemic, not personal. In the end, the shared symbols, the unconditional, unthinking, seemingly eternal loyalty are to, as Liz Fekete put it, one's own kith and kin, more than any charismatic leader who can facilitate it. The focus in this book has been on the triumphant rise of illiberalism in Central Europe, between the ‘European migration ciris’ of 2015–16, and the efforts of the opposition that gained visibility in late 2021. The illiberal revolt that Orbán and Kaczynski helped to unleash will, however, likely remain a powerful force for much of this century, and long after they are gone, because in the end illiberalism responds to global and local forces that are much more consequential than the personal whims of any individual, no matter how influential.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.089 | 0.028 |
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".