Academia under fire in Hungary

May 12, 2017
Albert-László Barabási

On 10 April, Hungarian President János Áder signed into law an amendment to the National Higher Education Law that would outlaw the Central European University (CEU). Although portrayed by the government as a purely administrative step, the “Lex-CEU” law is a strident attempt to curtail academic freedom and limit the independence of academic institutions.

Accredited in both the United States and Hungary, and operating in Budapest since 1991, CEU offers English-language master's and doctoral programs in fields from public policy to network science. CEU ranks around 300 in the World University Rankings, with several programs in the top 100 (no other Hungarian university reached the top 500, likely due in part to severe underfunding of higher education). CEU has become the school of choice for the region's brightest students, many of whom populate governments and nonprofit sectors of Eastern Europe.

CEU's academic independence, modeled on its U.S. peers, has angered the government, which portrays it as a hotbed of liberal thinking. It is hard to point to any event or action by the university that triggered this crisis. The official reason offered by the government remains puzzling. It argued that U.S.-based degrees offered by CEU are a comparative advantage, unmatched by local institutions. In its view, the new law creates an even playing field. This reasoning fooled no one. The law is widely seen as an attempt to gain electoral advantage by picking a fight with the university's founder, the Hungarian-born U.S. philanthropist George Soros, whose long-standing advocacy for open societies and migrants is at odds with the isolationist stand pursued by Prime Minister Viktor Orbán. The law's political nature is made manifest in the impossible, and potentially unconstitutional, conditions it imposes. It requires CEU to open a campus in New York State, where it is accredited, by October 2017, which is a practical impossibility. It also requires the university to be regulated by an agreement between Hungary and the U.S. federal government—ignoring the fact that education in the United States is under the jurisdiction of individual states. Unable to meet these requirements, CEU will lose its ability to admit new students next spring.

Lex-CEU follows the playbook of Russian President Vladimir Putin, who used similar legislative tactics against the European University in St. Petersburg, and mirrors attacks by members of the U.S. Congress against funding of political science. The masterminds of these attacks do not realize that academia is not a set of isolated interest groups but a tightly interconnected network committed to advancing knowledge. An attack on one of academia's nodes—an institution, a field, or a researcher—threatens the advancement of knowledge as a whole.

The heartwarming response to Lex-CEU reaffirms the power of this interconnectedness. Most academic leaders in Hungary, at great professional and personal risk, have spoken up in support of CEU, and the law prompted large street demonstrations in Budapest.

Despite the law's apparent finality, the battle is just beginning. The university's president has vowed that research and scholarship will continue. The European Parliament has opened an investigation into the law's legality and harmony with European Union laws. Within Hungary, the Supreme Court has been asked to rule on the law's constitutionality, although independence of the courts has been questionable. None of these efforts are likely to conclude by the fatal October deadline, which means that only coordinated and meaningful U.S. and European political pressure, at the highest level, can restore CEU's ability to enroll its next cohort of students.

CEU offers a test of Hungary's ability to guarantee academic institutions' long-term viability and commitment to educational excellence. It is a battle whose outcome will reverberate around the world. A loss will embolden those who aim to limit education and restrict free speech; a win will reaffirm academic freedom.

Published in Science 356:6338, 563 (2017)


Figure 1. How hard is to distinguish random from scale-free networks? To show how different are the predictions of the two modeling paradigms, the scale-free and that or the random network models, I show the degree distribution of four systems: Internet at the router level; Protein-protein interaction network of yeast; Email network; Citation network, together with the expected best Poisson distribution fit. It takes no sophisticated statistical tools to notice that the Poisson does not fit.
Box 3: All we need is love

If you have difficulty understanding the need for the super-weak, weakest, weak, strong and strongest classification, you are not alone. It took me several days to get it. So let me explain it in simple terms.

Assume that we want to find the word Love in the following string: "Love". You could of course simply match the string and call it mission accomplished. That, however, would not offer statistical significance for your match.

BC insist that we must use a rigorous algorithm to decide if there is Love in Love. And they propose one, that works like this: Take the original string of letters, and break it into all possible sub-strings: 


They call the match super-strong if at least 90% of these sub-strings matches Love. In this case we do have Love in the list, but it is only one of the 14 possible sub-strings, so Love is not super strong.  

They call the match super-weak if at least 50% of the strings matches the search string. Love is obviously not super-weak either.

At the end Clauset's algorithm arrives to the inevitable conclusion: There is no Love in Love.

The rest of us: Love is all you need

‍Figure 3. Differentiating model systems Curious about the reason the method adopted by BC cannot distinguish the Erdős-Rényi and the scale-free model, we generated the degree distribution of both models for N=5,000 nodes, the same size BC use for their test. We have implemented the scale-free model described in Appendix E of Ref [1], a version of the original scale-free model (their choice is problematic, btw, but let us not dwell on that now). In the plot we  show three different realizations for each network, allowing us to see the fluctuations between different realisations, which are small at this size. The differences between the two models are impossible to miss: The largest nodes in any of the Erdős-Rényi networks have degree less then 20, while the scale-free model generate hubs with hundreds of links. Even a poorly constructed statistical test could tell the difference. Yet,  38% of the time the method used by BC does not identify the scale-free model to be even ‘weak scale-free,’  while 51% of the time it classifies the ER model to be ‘weak scale-free.’


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