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Record W4415016829 · doi:10.1080/01596306.2025.2567659

Soft authoritarian transformation of higher education in Hungary: taming academic freedom with neoliberal precarity

2025· article· en· W4415016829 on OpenAlexaff
Zahra Jafarova

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrecarityHigher educationAuthoritarianismNeoliberalism (international relations)Academic freedomTransformation (genetics)

Abstract

fetched live from OpenAlex

This study explores how soft authoritarianism repurposes neoliberal reforms to reshape higher education in Hungary. While neoliberal reforms typically emphasize financial efficiency, in Hungary, they are instrumentalized to embed control over knowledge production. Drawing on qualitative interviews, the analysis traces three mechanisms: (1) governance change that shifts decision-making and public assets to private foundations led by regime-aligned actors, reducing transparency, and normalizing audit culture; (2) creating parallel institutions for research and teaching and redirecting funds toward regime-aligned scholarship while gatekeeping sensitive-to-the-regime-ideology research; (3) selective coercion enabled by authoritarian legalism, to signal punitive capacity for non-compliance. These dynamics produce a diversified academic freedom system where model-changed universities offer higher pay but discipline research, whereas academic freedom in public universities is better exercised yet under chronic underfunding. The research contributes to the literature on governance and academic freedom, illustrating how neoliberal precarity functions as a ‘soft authoritarian’ mechanism to control academia.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.423
Teacher spread0.386 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

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