1,490 research outputs found
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Does Inequality Migrate? The Development of Income Inequality across German states
Data availability statement: The data used in this study comes from the German Socio-Economic Panel (GSOEP), which is a comprehensive household survey conducted for more than 30 years. Oleg Badunenko and I are bound by the contract not to distribute data, so we can't share the data, but we would be happy to share detailed instructions on how to obtain the sample that was used. GSOEP is free of charge for academic researchers.Supporting Information is available online at: https://doi.org/10.1111/jors.12683 .Copyright © 2024 The Authors. This study analyzes the evolution of educational and occupational patterns among migrants and natives, as well as income inequality in Germany from 1985 to 2015. We show that despite migrants catching up in education, employment, and income with their native counterparts, unfavorable societal attitudes toward them have remained virtually unchanged, which can be attributed to Bourdieu's conceptualization of cultural inheritance. We find that while income inequality has increased significantly over the 30-year period, this trend varied considerably by the federal state and that migration did nothing to add to inequality. Since both the German economy and society rely on migrants, there is a strong need for the narratives toward migrants to be based on empirical evidence. The findings of this study hold migrant-related policy implications not only for Germany but also for other developed nations that rely on migrants as a labor force
Essays on Corporate Disclosure of Value Creation
Information on a firm’s business model helps investors understand an entity’s resource requirements, priorities for action, and prospects (FASB, 2001, pp. 14-15; IASB, 2010, p. 12). Disclosures of strategy and business model (SBM) are therefore considered a central element of effective annual report commentary (Guillaume, 2018; IIRC, 2011). By applying natural language processing techniques, I explore what SBM disclosures look like when management are pressed to say something, analyse determinants of cross-sectional variation in SBM reporting properties, and assess whether and how managers respond to regulatory interventions seeking to promote SBM annual report commentary. This dissertation contains three main chapters. Chapter 2 presents a systematic review of the academic literature on non-financial reporting and the emerging literature on SBM reporting. Here, I also introduce my institutional setting. Chapter 3 and Chapter 4 form the empirical sections of this thesis. In Chapter 3, I construct the first large sample corpus of SBM annual report commentary and provide the first systematic analysis of the properties of such disclosures. My topic modelling analysis rejects the hypothesis that such disclosure is merely padding; instead finding themes align with popular strategy frameworks and management tailor the mix of SBM topics to reflect their unique approach to value creation. However, SBM commentary is less specific, less precise about time horizon (short- and long-term), and less balanced (more positive) in tone relative to general management commentary. My findings suggest symbolic compliance and legitimisation characterize the typical annual report discussion of SBM. Further analysis identifies proprietary cost considerations and obfuscation incentives as key determinants of symbolic reporting. In Chapter 4, I seek evidence on how managers respond to regulatory mandates by adapting the properties of disclosure and investigate whether the form of the mandate matters. Using a differences-in-differences research design, my results suggest a modest incremental response by treatment firms to the introduction of a comply or explain provision to provide disclosure on strategy and business model. In contrast, I find a substantial response to enacting the same requirements in law. My analysis provides clear and consistent evidence that treatment firms incrementally increase the volume of SBM disclosure, improve coverage across a broad range of topics as well as providing commentary with greater focus on the long term. My results point to substantial changes in SBM reporting properties following regulatory mandates, but the form of the mandate does matter. Overall, this dissertation contributes to the accounting literature by examining how firms discuss a central topic to economic decision making in annual reports and how firms respond to different forms of disclosure mandate. Furthermore, the results of my analysis are likely to be of value for regulators and policymakers currently reviewing or considering mandating disclosure requirements. By examining how companies adapt their reporting to different types of regulations, this study provides an empirical basis for recalibrating SBM disclosure mandates, thereby enhancing the information set of capital market participants and promoting stakeholder engagement in a landscape increasingly shaped by non-financial information
Patterns and Variation in English Language Discourse
The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels
Nowadays, the use of intelligent systems to detect redundant information in
news articles has become especially prevalent with the proliferation of news
media outlets in order to enhance user experience. However, the heterogeneous
nature of news can lead to spurious findings in these systems: Simple
heuristics such as whether a pair of news are both about politics can provide
strong but deceptive downstream performance. Segmenting news similarity
datasets into topics improves the training of these models by forcing them to
learn how to distinguish salient characteristics under more narrow domains.
However, this requires the existence of topic-specific datasets, which are
currently lacking. In this article, we propose a new dataset of similar news,
SPICED, which includes seven topics: Crime & Law, Culture & Entertainment,
Disasters & Accidents, Economy & Business, Politics & Conflicts, Science &
Technology, and Sports. Futhermore, we present four distinct approaches for
generating news pairs, which are used in the creation of datasets specifically
designed for news similarity detection task. We benchmarked the created
datasets using MinHash, BERT, SBERT, and SimCSE models
A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation
Coordination in telephone-based remote interpreting
Telephone-based remote interpreting has come into widespread use in multilingual encounters, all the more so in times of refugee crises and the large influx of asylum-seekers into Europe. Nevertheless, the linguistic practices in this mode of communication have not yet been examined comprehensively. This article therefore investigates selected aspects of turn-taking and clarification sequences during semi-authentic telephone-interpreted counselling sessions for refugees (Arabic–German). A quantitative analysis reveals that limited audibility makes it more difficult for interpreters to claim their turn successfully; in most cases, however, turn-taking occurs smoothly. The trouble sources that trigger queries are mainly content-related and interpreters vary greatly in the ways they deal with such difficulties. Contrary to what one might expect, the study shows that coordination fails only rarely during telephone-based remote interpreting
Predicate Matrix: an interoperable lexical knowledge base for predicates
183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas
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An ontology for defining and characterizing demonstration environments
Demonstration Environments (DEs) are essential tools for testing and demonstrating new technologies, products, and services, and reducing uncertainties and risks in the innovation process. However, the terminology used to describe these environments is inconsistent, leading to heterogeneity in defining and characterizing them. This makes it difficult to establish a universal understanding of DEs and to differentiate between the different types of DEs, including testbeds, pilot-plants, and living labs. Moreover, existing literature lacks a holistic view of DEs, with studies focusing on specific types of DEs and not offering an integrated perspective on their characteristics and applicability in different contexts. This study proposes an ontology for knowledge representation related to DEs to address this gap. Using an ontology learning approach analyzing 3621 peer-reviewed journal articles, we develop a standardized framework for defining and characterizing DEs, providing a holistic view of these environments. The resulting ontology allows innovation managers and practitioners to select appropriate DEs for achieving their innovation goals, based on the characteristics and capabilities of the specific type of DE. The contributions of this study are significant in advancing the understanding and application of DEs in innovation processes. The proposed ontology provides a standardized approach for defining and characterizing DEs, reducing inconsistencies in terminology and establishing a common understanding of these environments. This enables innovation managers and practitioners to select appropriate DEs for their specific innovation goals, facilitating more efficient and effective innovation processes. Overall, this study provides a valuable resource for researchers, practitioners, and policymakers interested in the effective use of DEs in innovation
Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events
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Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202
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