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Record W4417087382 · doi:10.1080/01596306.2025.2599746

Special issue editorial: democratic decline and the changing state-university relationship in Europe

2025· article· en· W4417087382 on OpenAlexaff
Rasmus Harsbo, Zahra Jafarova, Ester Zangrandi

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
KeywordsNationalismCognitive reframingAutonomyIdeologyPoliticsGeopoliticsNeoliberalism (international relations)DemocracyNational identity

Abstract

fetched live from OpenAlex

Since the 1990s, European higher education has been reshaped by neoliberal policies promoting internationalization and competitiveness. Yet this trajectory now collides with resurgent nationalism and illiberal governance. Across Europe, governments have sought to limit academic freedom, politicize universities, and reclaim control over knowledge production, often by reframing global engagement as a threat to national identity and sovereignty. Far-right movements instrumentalize neoliberal mechanisms – such as funding and evaluation – to reward ideological allies and marginalize dissent. The resulting tensions reveal how universities have become arenas where struggles over democracy, globalization, and cultural values intersect. Bringing together comparative and national case studies, this special issue examines how these dynamics transform higher education’s political and institutional role. It shows that neoliberal and nationalist agendas increasingly intertwine, challenging assumptions about autonomy and universality while redefining the place of the university in Europe’s shifting geopolitical landscape.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.002
Science and technology studies0.0050.004
Scholarly communication0.0130.006
Open science0.0040.003
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0240.008

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 head0.030
GPT teacher head0.389
Teacher spread0.359 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2025
Admission routes1
Has abstractyes

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