The power of policy translators: New university governing bodies in Hungary and Poland
Bibliographic record
Abstract
The study investigates the reforms of university governing boards in Hungary and Poland. It seeks to fill that void and advance existing knowledge about the implementation of boards (councils) in CEE countries despite the great interest in HE dynamics in the region. The juxtaposition of the two countries is intentional because both share key characteristics, such as a common historical background (e.g. a communist past), geopolitical location (Central and Eastern Europe), the same institutional foundation for universities (i.e. the Humboldtian tradition) and domestic politics dominated by right-wing populism. With this in mind, it is interesting to note that the two countries, which are inspired by the same hegemonic policy ideas of NPM and had considerable similarities with respect to HE, arrived at different outcomes. This study therefore focuses on the process of policy translation and attempts to identify critical junctures that have led to structural divergence in the university governance model in the two countries. To achieve this, the research examines two parallel reforming processes that led to the introduction of new university governing bodies: consistories (2015) in Hungary and university councils (2018) in Poland.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".