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In den letzten Jahren haben in Deutschland mehrere Bundesländer Studiengebühren eingeführt. Seitdem wird in der Politik heftig über das Für und Wider diskutiert. Von den Gegnern wird häufig das Argument vorgebracht, Studiengebühren seien sozial ungerecht und würden insbesondere einkommensschwache Bevölkerungsschichten von einem Studium abhalten. Bestätigt werden sie von den Zahlen aus Umfragen, denen zufolge über 70 Prozent derjenigen, die sich gegen ein Studium entscheiden, finanzielle Gründe für einen Studienverzicht anführen. Befürworter halten dem entgegen, Studiengebühren seien angesichts knapper öffentlicher Kassen unbedingt notwendig, da nur so die Qualität der Lehre aufrechterhalten bzw. verbessert werden könne. Zudem würden Studiengebühren die sozialen Bildungsbarrieren keineswegs erhöhen, was auch daran erkennbar sei, dass infolge der Studiengebühren die Zahl der Studierenden nicht zurückgegangen ist. Der vorliegende Artikel leistet einen Beitrag zu dieser Debatte, indem er die an einer Hochschule in Nordrhein-Westfalen gesammelten Erfahrungen mit Studiengebühren auswertet
Glasfaserbasierte Breitbandnetze werden in Deutschland auch aufgrund der Wirtschaftskrise nur sehr zögerlich ausgebaut. Markus Fredebeul-Krein hält eine staatliche Subventionierung der Breitbandnetze nur bedingt für gerechtfertigt. Private Investoren benötigen jedoch dringend Planungssicherheit durch verlässliche Regulierungsvorschriften.
This paper develops an investment/pricing model for the deployment of basic broadband networks which, along with other applications, is applicable to public–private partnership projects. In particular, a new investment model is suggested to be used for finance deployment over a longer term by enabling both private and public investors to participate in the roll-out of next generation access (NGA) infrastructure. This so-called “long-term risk sharing concept” has several notable benefits compared with the traditional regulatory approach. Above all, the model enables both private operators and public authorities to share the risk of investing in NGA infrastructure. Thus the model offers a way for public authorities to achieve a timely and countrywide roll-out of NGA networks, including in areas where NGA investment would otherwise not occur.
Next Generation Access Networks: Why is there a higher risk of investment and how to deal with it?
(2009)
AI-based systems are nearing ubiquity not only in everyday low-stakes activities but also in medical procedures. To protect patients and physicians alike, explainability requirements have been proposed for the operation of AI-based decision support systems (AI-DSS), which adds hurdles to the productive use of AI in clinical contexts. This raises two questions: Who decides these requirements? And how should access to AI-DSS be provided to communities that reject these standards (particularly when such communities are expert-scarce)? This chapter investigates a dilemma that emerges from the implementation of global AI governance. While rejecting global AI governance limits the ability to help communities in need, global AI governance risks undermining and subjecting health-insecure communities to the force of the neo-colonial world order. For this, this chapter first surveys the current landscape of AI governance and introduces the approach of relational egalitarianism as key to (global health) justice. To discuss the two horns of the referred dilemma, the core power imbalances faced by health-insecure collectives (HICs) are examined. The chapter argues that only strong demands of a dual strategy towards health-secure collectives can both remedy the immediate needs of HICs and enable them to become healthcare independent.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.