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.
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 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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".