Mapping institutional changes in higher education: the comparative analysis of the effects of democratic backsliding
Bibliographic record
Abstract
The world has witnessed democratic decline in 23 countries worldwide during the last decade (Freedom House, 2019) in the context of rising nationalism and right-wing populism (Fraser, 2017; Robertson, 2018, 2020). The political importance of this topic is rooted in the fact that higher education is one of the most crucial public goods (Marginson, 2007, 2017) and governments tend to exercise tighter control over HEIs while democratic conditions worsening (Perry, 2015). Although many studies have examined the effects of the transition to democracy on higher education globally (O’Donnell et al., 2013; O’Donnell et al., 1986, Salto, 2020), very few have studied the reverse trend – democratic backsliding. Given that university autonomy is a wider term that encompasses the practises undertaken by universities to operate, researching its aspects, and assessing the true implications of democratic backsliding on universities represents an important field for current and future research. My research investigates the impact of democratic backsliding on the university autonomy, by examining the cases of Turkey, Hungary, and Poland. These countries were considered democracies until the 2010s, but they are increasingly moving away from democracy (Freedom House, 2020). The study draws on an extensive analysis of publicly accessible government laws and regulations, university decrees, mission statements, political pamphlets, online media sources and interviews, and grey literature to analyze institutional responses as well as field work and interviews. I employ neoliberal authoritarianism and historical institutionalism as a framework to investigate the critical junctures and institutional changes affecting appointive (hiring, promotion, and dismissal of staff), financial (funding levels and criteria, preparation and allocation of the university budget, and accountability), and academic (access, curriculum, degree requirements, and academic freedom) autonomy (Ordorika, 2003).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".