The Consequences of Political Mislabelling: How Hungary Challenges the Left-Right LRECON Spectrum
Bibliographic record
Abstract
Hungary’s Fidesz party, led by Viktor Orbán, challenges conventional political categorization, often leading to mislabeling of its political ideological position in media and academia. How can one make sense of its true ideological position and the consequences of this mislabeling? This paper assesses the political positioning of Fidesz through datasets examining the Central and Eastern Europe region, in comparative analysis with Western Europe. The findings reveal that Fidesz, often labeled as “radical right”, exhibits left-leaning economic policies alongside authoritarian social stances. This mislabeling exposes the limitations of the LRECON (left-right) axis in understanding certain political landscapes and argues for the incorporation of the GALTAN (authoritarian-libertarian) axis. Mislabeling Fidesz as right-wing allows political discourse by Orbán and the global community to obscure the party’s authoritarian tendencies. This paper suggests that recognizing the multidimensional nature of politics beyond the conventional left-right framework and reassessing classification methods may lead to more accurate political categorization to help expose and identify authoritarian parties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".