In der vorliegenden Arbeit wurde sich mit der Frage beschäftigt, ob und wenn ja inwiefern das spanische que, neben seinen klassischen Verwendungsweisen als Pronomen und Konjunktion, als Diskursmarker (DM) fungieren kann, also ob que in bestimmten Kontexten seinen propositionalen Gehalt verliert und rein diskursive Funktionen übernimmt. Es wurden 128 Beispiele von satzinitialen que untersucht, welche sich zunächst nicht eindeutig als grammatisches Element klassifizieren lassen. Die Beispiele entstammen einem Korpus, welches auf einem auf Grundlage der zweiten Staffel der Netflix-Serie “Élite” erstellten Transkript basiert. Das Material wurde anhand von fünf auf Grundlage der Forschungsliteratur erstellten Kriterien analysiert und je nach Erfüllung oder Nicht-Erfüllung in die Kategorien “nicht pragmatikalisiert” (NP), “teilweise pragmatikalisiert” (TP) und “pragmatikalisiert” (P) eingeordnet. Innerhalb jeder dieser Kategorien wurde(n) die entsprechende(n) grammatische(n) bzw. pragmatische(n) Funktion(en) spezifiziert und die Ergebnisse in einem Raster zusammengetragen. Für die Funktionszuordnung in der Kategorie (P) wurde hierbei auf die DM-Klassifizierung von Martín Zorraquino und Portolés 1999 zurückgegriffen und hierbei teilweise noch einmal weiter spezifiziert. Bei der Analyse haben sich 89 als P, 34 als TP und fünf Beispiele als NP herausgestellt. Von den 89 als P eingestuften que wurde der Großteil (84) als “comentador” beschrieben - als DM, der einen Kommentar einführt. So wurden insgesamt 72 que als DM eingestuft, die einen erklärenden Kommentar einleiten. Es wurde hiermit eine objektive Einstufung von que als DM erreicht, welche gleichzeitig erste Aufschlüsse über die spezifischen Funktionen von que als DM gibt. Die Nutzung konkreter Kriterien zur Analyse von potentiellen DM gewährleistet Objektivität und leistet einen Beitrag zur Systematisierung der teils von Uneinigkeiten und Interpretationen geprägten DM-Forschung.

Der Bildungshausbau ist Thema aktueller Debatten in der Stadtentwicklung und Stadtplanung sowie in der Pädagogik. Viele Expert*innen beschäftigen sich in Studien mit Fragen zu gutem und gelingendem Schulbau. Die Anforderungen der Gesellschaft an Bildungshäuser verändern sich, wenn in ganztägigen Schulformen nicht nur Unterricht, sondern auch Freizeitbetreuung für die Schülerinnen und Schüler stattfinden soll. Gleichzeitig soll Schule ein Ort der Begegnung und Kommunikation, des sozialen Lernens und der Kooperation sein. Schule ist in vielfacher Hinsicht in Bewegung. Um mit den Veränderungen und Ansprüchen Schritt zu halten, steht der Bildungshausbau immer wieder vor Herausforderungen. Einerseits werden Leuchtturmprojekte geschaffen, andererseits entstehen nach wie vor Bildungsbauten, die den gegenwärtigen Anforderungen und zukünftigen Entwicklungen nicht gerecht werden. An dieser Stelle setzt die vorliegende Arbeit an, die nicht neue Normen zu gutem Schulbau vorlegt, sondern in einer qualitativen empirischen Studie nach den pädagogischen Vorstellungen von Beteiligten im Bildungshausbau und den typischen Entwicklungen im Planungsprozess fragt. Der vorliegenden Fallstudie wurde die dokumentarische Methode als Auswertungsverfahren zugrunde gelegt. Gegenstand der Untersuchung waren zwei Bildungsbauten eines Großbauprojektes. Im Zuge der Auswertung erfolgten eine Analyse der Projektstrukturen und eine Analyse der Deutungsmuster der befragten Akteur*innen, die in einer zusammen¬führenden Ergebnisdarstellung in Form eines Handlungs-Struktur-Gefüges mündeten. Es werden Einblicke in Zusammenhänge von Handlungen der Beteiligten und Projektstrukturen gegeben, wie sie sich gegenseitig beeinflussen oder im Prozessverlauf verändern. Die Auswertung zeigt, dass Transferproblematiken zwischen Wissenschaft und Praxis nach wie vor bestehen. Besonderes Gewicht bei Planungsentscheidungen haben finanzielle, zeitliche und architektonische Strukturen. Nur wenige pädagogische Vorstellungen bzw. Deutungsmuster können in Erscheinung treten.

Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows. While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture. There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development. The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing. One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods. The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points. To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library. RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance. The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction. To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation. For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data. Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.

The goal of this dissertation is to contribute to the corporate foresight research field by investigating capabilities, practices, and challenges particularly in the context of interorganizational settings and networked organizations informed by the theoretical perspectives of the relational view and dynamic capabilities. Firms are facing an increasingly complex environment and highly complex product and service landscapes that often require multiple organizations to collaborate for innovation and offerings. Public-private partnerships that are targeted at supporting this have been introduced by policy-makers in the recent past. One example for such a partnership is the European Institute of Innovation and Technology (EIT) with multiple Knowledge and Innovation Communities (KICs). The EIT has been initiated by the European Commission in 2008 with the ambition of addressing grand societal challenges, driving innovativeness of European companies, and supporting systemic change. The resulting network organizations are managed similarly to corporations with managers, boards, and firm-like governance structures. EIT Digital as one of the EIT KICs are a central case of this work. Research in this dissertation was based on the expectation that corporate foresight activities will increasingly be embedded in such interorganizational settings and a) can draw on such settings for the benefit of themselves and b) may contribute to shared visions, trust building and planning in these network organizations. In this dissertation the EIT Digital (formerly EIT ICT Labs) is a central case, supplemented with insights from three additional cases. I draw on the rich theoretical understanding of the resource-based view, dynamic capabilities, and particularly the relational view to further the discussion in the field of corporate foresight—defined as foresight in organizations in contrast to foresight with a macro-economical perspective—towards a relational understanding. Further, I use and revisit Rohrbeck’s Maturity Model for the Future Orientation of Firms as conceptual frame for corporate foresight in interorganizational settings. The analyses—available as four individual publications complemented by on additional chapter—are designed as exploratory case studies based on multiple data sources including an interview series with 49 persons, two surveys (N=54, n=20), three supplementary interviews, access to key documents and presentations, and observation through participation in meetings and activities of the EIT Digital. This research setting allowed me to contribute to corporate foresight research and practice by 1) integrating relational constructs primarily drawn from the relational view and dynamic capabilities research into the corporate foresight research stream, 2) exploring and understanding capabilities that are required for corporate foresight in interorganizational and networked organizations, 3) discussing and extending the Maturity Model for network organizations, and 4) to support individual organizations to tie their foresight systems effectively to networked foresight systems.

Militärgeschichte und Wissensgeschichte bilden zwei in den vergangenen Jahrzehnten international prosperierende Forschungsfelder, die bislang aber selten miteinander in Dialog getreten sind. Das Themenheft nimmt dies zum Anlass, exemplarisch die Potentiale wissensgeschichtlicher Perspektiven für die (früh-)neuzeitliche Militärgeschichte auszuloten und dabei zugleich den bislang oft unreflektierten Zäsurcharakter der Jahre um 1800 kritisch zu beleuchten. Gab es eine eigene militärische Wissenskultur oder inwieweit partizipierte das Militär an den zivilen Wissenskulturen seiner sozialen Umwelt? Welche Akteure, welche Praktiken und welche Medien spielten eine Rolle bei der Verwissenschaftlichung des Militärischen im Wandel von der Kriegskunst zur Kriegswissenschaft? Gerade der geweitete analytische Horizont der Wissensgeschichte ermöglicht es, der Vielfalt der Wissensformen Rechnung zu tragen und entsprechende Entwicklungen angemessen in ihren historischen Kontexten zu verorten. Darüber hinaus bietet der epochenübergreifende Zuschnitt die Chance, nicht nur Brüche, sondern auch mögliche Kontinuitäten zwischen frühneuzeitlichem und neuzeitlichem Militärwesen sowie dessen Beziehungen zum Wissen aufzuzeigen und etwaige Ungenauigkeiten oder historiographisch bedingte Verkürzungen durch neue Akzentsetzungen zu korrigieren.