Tweeting the academic resistance in Turkey and Hungary: from cultural crisis to defending the nation
Bibliographic record
Abstract
This paper explores the reemergence of universities as spaces for the contestation of government policies in an era of illiberalism, rising nationalism and populism. We focus on high-profile contestations occurring at two public universities in Turkey and Hungary: Boğaziçi University and the University of Theatre and Film Arts, respectively. At both universities, attacks on democratic governance and liberal-democratic values have resulted in academic resistance that have played out on physical campuses and in online spaces. We conceptualise a virtual space as a space of contestation and analyse social media data to identify the dominant narratives of governments and their supporters on one side and resistance supporters on the other that gives the way to two major narratives: while right-wing imaginaries and pro-government actors frame the liberal and pro-freedom inclinations of universities as a ‘threat’ to the nation, students and faculty portray the government as the true threat to their rights and freedoms and knowledge production within what was once a ‘safe space’ in universities, thus framing themselves as the ‘true guardians’ of their institutions. Our analysis shows how universities have become contemporary flashpoints in and upon which broader political contestations over national culture play out.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".