Mit Bürger_innenentscheiden gegen Bergbau und Fracking

Dieses Paper analysiert den Einsatz direktdemokratischer Instrumente in Konflikten um Bergbau und Fracking. Unter welchen Bedingungen greifen Protestgruppen zu diesen Instrumenten und wie setzen sie diese ein? Was sind die Erfolgsbedingungen? Dazu definiert es zunächst den Begriff „direkte Demokratie“ und erklärt die wichtigsten Instrumente. Darauf folgt ein kurzer Überblick über Rohstoffabbau und direktdemokratische Entscheidungsverfahren in den Ländern, deren Bürger_innen Referenden besonders oft nutzen, um Bergbau- und Bohrprojekte zu stoppen. Vier Fälle werden genauer betrachtet. Die Ergebnisse der Analyse und ein Ausblick auf weitere Forschungsfragen schließen das Paper ab

Complaining as Reflective Practice in TESOL Teacher–Mentor Post-Observation Meetings

This study investigates interactions between a novice language teacher and TESOL practicum mentor during a series of post-observation meetings, focusing on how and why the teacher engages in complaining. We draw upon conversation analysis and narrative analysis to look at how the teacher’s complaints are developed and managed, as well as what they accomplish, within the institutional context. The data show the novice teacher uses a variety of interactional resources to construct complaints about her co-teacher, a peer observer, and the practicum course workload. We argue that complaints are relevant to reflective practice and show how the teacher’s complaints work to express beliefs about teaching and learning and to defend her competence and moral values as a novice teacher. Based on our analysis, we discuss implications for mentor practice

Modeling styles in conceptual data modeling: Reflecting observations in a series of multimodal studies

A modeling style characterizes a modeler’s sequencing of processing a modeling task in terms of applying the modeling language and its language concepts while constructing a conceptual model. Presently, surprisingly little is known about the different modeling styles modelers exhibit when performing conceptual data modeling. In this research, we combine complementary modes of observation including audio-visual protocols, recorded modeler-tool interactions, and pre-/post-modeling surveys of modelers to identify modeling styles in 24 data modeling processes performed by modelers at different stages of experience in data modeling. Our study identifies and characterizes three distinct modeling styles refining our current knowledge about data modeling processes and informing design science research on style-specific, targeted modeling (software tool) support for data modelers

Integrating value-adding mobile services into an emergency management system for tourist destinations

Innovations are important for the development of firms and whole economies. Several theoretic approaches are therefore dealing with innovation and the motivation of firms to motivate. The Resource-based View of the Firm views innovations to be motivated by the use of slack resources while the Behavioral Theory of the Firm predicts problemistic search as an important motivation for innovative maneuvers. Recently, Pitelis proposes an integration of both theories to better explaining the motivation for innovation. This paper empirically tests the predictions from these theories using multiple case studies among small and medium enterprises. The results show that firms’ motivation to innovate is best explained using a combination of both theories

Towards Quantifying Sampling Bias in Network Inference

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labelled sample? How should the network be sampled, and what effect does it have on inference error? How does the structure of the network impact the sampling strategy? We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labelled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. These findings suggest that the impact of network structure on relational classification is more complex than previously thought.Comment: Accepted at the International workshop on Mining Attributed Networks (MATNet) workshop at WWW201

Performance characteristics of a rapid immunochromatographic assay for detection of pandemic influenza A (H1N1) virus in children

Rapid tests for diagnosis of influenza are valuable assets in the management of influenza in pediatric patients. However, test performance fluctuates with virus subtypes. We assessed the test characteristics of Influenzatop®, a rapid immunochromatographic influenza A and B test, in detecting pandemic 2009 influenza A (H1N1) in children up to 18years of age, using reverse transcriptase polymerase chain reaction (RT-PCR) as the gold standard. Three hundred and one pediatric outpatients with influenza-like illness were included into the study. Overall sensitivity of Influenzatop® was 64% (95% confidence interval (CI) 56-71%) but increased to 92% (95% CI, 80-97%) when performed between 24 and 48h after onset of symptoms. Positive Influenzatop® results among RT-PCR-positive patients were associated with higher viral load. No significant variation in test performance could be detected when analyzed by age and high versus low prevalence period. Overall test specificity was 99% (95% CI, 95-100%); positive and negative predictive values were 98% (95% CI, 93-99%) and 70% (95% CI, 63-76%), respectively. Conclusion: Influenzatop® rapid influenza test is a sound tool in the diagnosis of H1N1 in pediatric patients when employed 24-48h after onset of symptom

Performance characteristics of a rapid immunochromatographic assay for detection of pandemic influenza A (H1N1) virus in children.

UNLABELLED Rapid tests for diagnosis of influenza are valuable assets in the management of influenza in pediatric patients. However, test performance fluctuates with virus subtypes. We assessed the test characteristics of Influenzatop®, a rapid immunochromatographic influenza A and B test, in detecting pandemic 2009 influenza A (H1N1) in children up to 18 years of age, using reverse transcriptase polymerase chain reaction (RT-PCR) as the gold standard. Three hundred and one pediatric outpatients with influenza-like illness were included into the study. Overall sensitivity of Influenzatop® was 64% (95% confidence interval (CI) 56-71%) but increased to 92% (95% CI, 80-97%) when performed between 24 and 48 h after onset of symptoms. Positive Influenzatop® results among RT-PCR-positive patients were associated with higher viral load. No significant variation in test performance could be detected when analyzed by age and high versus low prevalence period. Overall test specificity was 99% (95% CI, 95-100%); positive and negative predictive values were 98% (95% CI, 93-99%) and 70% (95% CI, 63-76%), respectively. CONCLUSION Influenzatop® rapid influenza test is a sound tool in the diagnosis of H1N1 in pediatric patients when employed 24-48 h after onset of symptoms

Virtuelle Competence Center – Verteilte Kompetenzen vernetzen und nutzbar machen.

Aus der Zusammenfassung: "Produkt- und Prozessinnovationen sind im heutigen Wettbewerbsumfeld zunehmend schnelllebiger. Damit steigt der Aufwand für die Erhaltung und den Ausbau des vorhanden Kompetenz- bzw. Wissensvorsprungs. Insbesondere die kosteneffiziente Nutzung und Erschließung unternehmensweit oder firmenübergreifend verteilter Kompetenzen und Wissensressourcen wird entscheidend für den Markterfolg.

Explaining classification performance and bias via network structure and sampling technique

Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available