Regional Income Convergence in the Enlarged Europe, 1995-2000: A Spatial Econometric Perspective

This paper adopts a spatial econometric perspective to analyse regional convergence of per capita income in Europe in 1995 to 2000 and, moreover, relaxes the assumption of a single steady-state growth path which appears to be out of tune with reality of empirical dynamics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional ß-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature. --European Regions,Income Convergence,Spatial Econometrics

Comparing different accelerometer cut-points for sedentary time in children

Actigraph accelerometers are hypothesized to be valid measurements for assessing children\u27s sedentary time. However, there is considerable variation in accelerometer cut-points used. Therefore, we compared the most common accelerometer sedentary cut-points of children performing sedentary behaviors. Actigraph Actitrainer uniaxial accelerometers were used to measure children\u27s activity intensity (29 children, 5-11 years old) during different activities, namely playing computer games, nonelectronic sedentary games, watching television and playing outdoors. A structured protocol was the criterion for assessing the validity of four common cut-points (100, 300, 800, 1100 counts/minute). The median counts during all sedentary behaviors were below the lowest comparison cut-point of 100 cpm. The 75th percentile values for the sedentary behaviors were always below the cut-point of 300 cpm. Our results suggest that the cut-point of <100 cpm is the most appropriate

Regional Income Convergence in the Enlarged Europe, 1995-2000: A Spatial Econometric Perspective

This paper adopts a spatial econometric perspective to analyse regional convergence of per capita income in Europe in 1995 to 2000 and, moreover, relaxes the assumption of a single steady-state growth path which appears to be out of tune with reality of empirical dynamics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional ß-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

Regional Income Convergence in Europe, 1995-2000: A Spatial Econometric Perspective

Questions of convergence have received increasing attention in recent years, in light of the pressure for greater integration and enlargement of the European Union [EU] to countries in Central and Eastern Europe [CEE]. This paper looks at the evidence for convergence of per capita income between regions in Europe in the second half of the 1990s, when economic recovery in CEE gathered pace. The analysis is based on the simplest of the models, the unconditional ß-convergence model and shows that the classical test methodology is ill-designed due to two reasons. First, it cannot identify groupings of regional economies that are converging. Second, it neglects spatial effects that represent interregional interactions and spatial spillovers. The paper suggests a much richer and theoretically more satisfactory approach that is in line with both the notions of club convergence and spatial dependence, and reflects recent developments in spatial econometrics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional beta-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature

Regional Income Convergence in Europe, 1995-2000: A Spatial Econometric Perspective

Questions of convergence have received increasing attention in recent years, in light of the pressure for greater integration and enlargement of the European Union [EU] to countries in Central and Eastern Europe [CEE]. This paper looks at the evidence for convergence of per capita income between regions in Europe in the second half of the 1990s, when economic recovery in CEE gathered pace. The analysis is based on the simplest of the models, the unconditional ß-convergence model and shows that the classical test methodology is ill-designed due to two reasons. First, it cannot identify groupings of regional economies that are converging. Second, it neglects spatial effects that represent interregional interactions and spatial spillovers. The paper suggests a much richer and theoretically more satisfactory approach that is in line with both the notions of club convergence and spatial dependence, and reflects recent developments in spatial econometrics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional beta-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature

Challenges in the automatic analysis of students' diagnostic reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

Challenging the Postwar Narrative: The Art and Agenda of Boris Lurie

Art history is shaped, studied, and taught based on narratives, artistic movements, and the biographies of celebrated artists. While contributing to an understanding of prevalent traditions and artists working in those traditions, these narratives are also constructions of inclusion and exclusion that establish art historical placement for certain artists while relegating others to historical obscurity. It is clear what happens to the critical fortunes of artists who are placed within these narratives. Yet what happens to the artists who do not fit within any of the categories established by these constructions? Are they then to be understood as simply minor artists or perhaps even “outsider artists?” Using the example of Boris Lurie and his critical fortune within the context of the standard art historical narrative of American art of the post World War Two period, this thesis argues for an expanded vision of modern and contemporary art that would accommodate lesser-known artists and offer a nuanced understanding of what American art has been after 1945

Analysis of automatic annotation suggestions for hard discourse-level tasks in expert domains

Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks

FAMULUS: interactive annotation and feedback generation for teaching diagnostic reasoning

Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student’s diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases