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In 2022, there were 4.62 billion social media users worldwide. Social media generates a wealth of data which migration scholars have recently started to explore in pursuit of a variety of methodological and thematic research questions. Scholars use social media data to estimate migration stocks, forecast migration flows, or recruit migrants for targeted online surveys. Social media has also been used to understand how migrants get information about their planned journeys and destination countries, how they organize and mobilize online, how migration issues are politicized online, and how migrants integrate culturally into destination countries by sharing common interests. While social media data drives innovative research, it also poses severe challenges regarding data privacy, data protection, and methodological questions relating to external validity. In this chapter, I briefly introduce various strands of migration research using social media data and discuss the advantages, disadvantages, and opportunities.
Enhancing higher entrepreneurship education: insights from practitioners for curriculum improvement
(2024)
Curricula for higher entrepreneurship education should meet the requirements of both a solid theoretical foundation and a practical orientation. When these curricula are designed by education specialists, entrepreneurs are usually not consulted. To explore practitioners’ curricular recommendations, we conducted 73 semi-structured interviews with entrepreneurs with at least five years of professional experience. We collected 49 items for teaching and learning objectives, 37 for contents, 28 for teaching methods, and 17 for assessment methods. The respondents are convinced that students should acquire solid knowledge in business and management, legal issues, and entrepreneurship. For the latter, only some core aspects are provided. The entrepreneurs put greater emphasis on entrepreneurial skills and attitudes and consider experiential learning designs as most suitable, both in the secure setting of the classroom and in real life. The findings can help reflect on current entrepreneurship curriculum designs.
Existing curricula for entrepreneurship education do not necessarily represent the best way of teaching. How could entrepreneurship curricula be improved? To answer this question, we aim to identify and rank desirable teaching objectives, teaching contents, teaching methods, and assessment methods for higher entrepreneurship education. To this end, we employ an international real-time Delphi study with an expert panel consisting of entrepreneurship education instructors and researchers. The study reveals 17 favorable objectives, 17 items of content, 25 teaching methods, and 15 assessment methods, which are ranked according to their desirability and the group consensus. We contribute to entrepreneurship curriculum research by adding a normative perspective.
Germany’s relatively stable party system faces a new left-authoritarian challenger: Sahra Wagenknecht’s Bündnis Sahra Wagenknecht (BSW) party. First polls indicate that for the BSW, election results above 10% are within reach. While Wagenknecht’s positions in economic and cultural terms have already been discussed, this article elaborates on another highly relevant feature of Wagenknecht, namely her populist communication. Exploring Wagenknecht’s and BSW’s populist appeal helps us to understand why the party is said to also have potential among seemingly different voter groups coming from the far right Alternative for Germany (AfD) and far left Die Linke, which share high levels of populist attitudes. To analyse the role that populist communication plays for Wagenknecht and the BSW, this article combines quantitative and qualitative methods. The quantitative analysis covers all speeches (10,000) and press releases (19,000) published by Die Linke members of Parliament (MPs; 2005–2023). The results show that Wagenknecht is the (former) Die Linke MP with the highest share of populist communication. Furthermore, she was also able to convince a group of populist MPs to join the BSW. The article closes with a qualitative analysis of BSW’s manifesto that reveals how populist framing plays a major role in this document, in which the political and economic elites are accused of working against the interest of “the majority”. Based on this analysis, the classification of the BSW as a populist party seems to be appropriate.
A new challenger seeks to enter the German party system: Bündnis Sahra Wagenknecht (BSW). With her new party, former Die Linke politician Sahra Wagenknecht combines a left-authoritarian profile (economically left-leaning, but culturally conservative) with anti-US, pro-Russia and anti-elitist stances. This article provides the first large-n academic study of the voter potential of this new party by using a quasi-representative sample (n = 6,000) drawn from a Voting Advice Application-like dataset that comes from a website designed to explore the Bündnis Sahra Wagenknecht’s positions. The results show that congruence with foreign policy positions and anti-elitism are strong predictors of the propensity to vote for the Bündnis Sahra Wagenknecht. In contrast, social/welfare and immigration policies are less predictive for assessing the party’s potential. Among the different socio-demographic groups, the Bündnis Sahra Wagenknecht has a strong potential among baby boomers, the less educated and East Germans. Regarding party voters, the Bündnis Sahra Wagenknecht is favoured by supporters of some minor parties like dieBasis, Freie Wähler and Die PARTEI, but also non-voters. Among the established parties, the party’s potential is high among Die Linke voters and, to a lesser extent, voters of the Social democrats (SPD) and Alternative for Germany (AfD). A potential below the average is reported for the supporters of the Liberals (FDP) and Christian Democrats (CDU/CSU) and most clearly for Green and Volt voters.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.
«Musik erfinden und gestalten» hat grosses musikpädagogisches Potenzial: mit Klängen experimentieren, ein Gespür für dramaturgische Verläufe entwickeln, nonverbal kommunizieren – Musik erfinden und gestalten eröffnet ein breites Feld musikalischer Aktivitäten und Erfahrungsmöglichkeiten. Doch im regulären Musikunterricht in der Volksschule der Schweiz sind produktionsdidaktische Ansätze noch eher die Ausnahme und Musiklehrkräften fehlt es an Anleitungsstrategien.
Für das vorliegende Buch untersuchte der Autor in Form einer Design-based-Research-Studie, wie Primarlehrkräfte ihre Anleitungsstrategien bei der Durchführung von musikalischen Gestaltungsprozessen in ihren Schulklassen schrittweise entwickeln. Dabei begleitete der Forscher die Lehrkräfte in der schulischen Praxis und intervenierte gezielt mit Reflexionsimpulsen, um den Professionalisierungsprozess zu unterstützen.
Daraus wurden drei Reflexionstools generiert: Das Reflexionstool try-outs beinhaltet konkrete Handlungsanregungen und Reflexionsfragen für das Anleiten musikalischer Gestaltungsprozesse. Das Onlinetool improspider ist ein Selbstreflexionsinstrument zur Einschätzung personaler Orientierungen. Das Kompetenzmodell Kompetenzflyer bietet eine Reflexionsfolie für die Ansteuerung eigenständiger Kompetenzerwerbsschritte.
Die Reflexionstools sind außerdem online in Form eines Lernobjekts verfügbar.
Organic solar cells (OSCs) represent a new generation of solar cells with a range of captivating attributes including low-cost, light-weight, aesthetically pleasing appearance, and flexibility. Different from traditional silicon solar cells, the photon-electron conversion in OSCs is usually accomplished in an active layer formed by blending two kinds of organic molecules (donor and acceptor) with different energy levels together.
The first part of this thesis focuses on a better understanding of the role of the energetic offset and each recombination channel on the performance of these low-offset OSCs. By combining advanced experimental techniques with optical and electrical simulation, the energetic offsets between CT and excitons, several important insights were achieved: 1. The short circuit current density and fill-factor of low-offset systems are largely determined by field-dependent charge generation in such low-offset OSCs. Interestingly, it is strongly evident that such field-dependent charge generation originates from a field-dependent exciton dissociation yield. 2. The reduced energetic offset was found to be accompanied by strongly enhanced bimolecular recombination coefficient, which cannot be explained solely by exciton repopulation from CT states. This implies the existence of another dark decay channel apart from CT.
The second focus of the thesis was on the technical perspective. In this thesis, the influence of optical artifacts in differential absorption spectroscopy upon the change of sample configuration and active layer thickness was studied. It is exemplified and discussed thoroughly and systematically in terms of optical simulations and experiments, how optical artifacts originated from non-uniform carrier profile and interference can manipulate not only the measured spectra, but also the decay dynamics in various measurement conditions. In the end of this study, a generalized methodology based on an inverse optical transfer matrix formalism was provided to correct the spectra and decay dynamics manipulated by optical artifacts.
Overall, this thesis paves the way for a deeper understanding of the keys toward higher PCEs in low-offset OSC devices, from the perspectives of both device physics and characterization techniques.
Von Koscher bis Frutarismus
(2024)