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Record W2808542627 · doi:10.1080/21699763.2018.1483256

Radical right framing of social policy in Hungary: between nationalism and populism

2018· article· en· W2808542627 on OpenAlexaff
Nicole V.T. Lugosi

Bibliographic record

VenueJournal of International and Comparative Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFraming (construction)NationalismPopulismPolitical sciencePolitical economyCommunismChauvinismSociologyGender studiesLawPoliticsHistory

Abstract

fetched live from OpenAlex

Abstract The populist radical right (PRR) is increasingly associated with welfare chauvinism, but the literature mainly focuses on Western and Northern European cases. Turning attention to Central Eastern Europe, this article investigates how PRR parties in Hungary frame welfare issues in five social policy areas from 2010 to 2016. This is done through a critical frame analysis applied to party manifestos and State of the Nation speeches by the Fidesz and Jobbik parties. Special care is taken to delineate the interlocking but not interchangeable concepts of nationalism and populism, as recent research asserts this distinction is often overlooked. The main findings are threefold: First, these parties articulate their positions chiefly through nationalist rather than populist framing; Second, while Hungary's PRR exhibits welfare chauvinist framing similar to Western and Northern Europe, a main difference detected was the role of the communist legacy; Third, beyond the article's original goals, the findings revealed a strong connection between nationalist framing and the role of gender, suggesting that the two are not mutually exclusive.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.010
Scholarly communication0.0050.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.407
Teacher spread0.354 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2018
Admission routes1
Has abstractyes

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