This study attempts to shed light on the efficacy of crowdsourcing as a means of translating web content in Canada. Within, we seek to explore and understand if a model can be created that can estimate the effectiveness of crowdsourced translation as a means of bridging the Canadian Linguistic Digital Divide. To test our hypotheses and models, we use structural equation modeling techniques coupled with confidence intervals for comparing experimental crowdsourced translation to both professional and machine translation baselines. Furthermore, we explore a variety of factors which influence the quality of the experimental translations, how those translations performed in the context of their source text, and the ways in which the views of the quality of the experimental translations were measured before and after participants were made aware of how the experimental translations were created.

In pay-as-you-go data integration, feedback can inform the regeneration of different aspects of a data integration system, and as a result, helps to improve the system's quality. However, feedback could be expensive as the amount of feedback required to annotate all the possible integration artefacts is potentially big in contexts where the budget can be limited. Also, feedback could be used in different ways. Feedback of different types and in different orders could have different effects in the quality of the integration. Some feedback types could give rise to more benefit than others. There is a need to develop techniques to collect feedback effectively. Previous efforts have explored the benefit of feedback in one aspect of the integration. However, the contributions have not considered the benefit of different feedback types in a single integration task. We have investigated the annotation of mapping results using crowdsourcing, and implementing techniques for reliability. The results indicate that precision estimates derived from crowdsourcing improve rapidly, suggesting that crowdsourcing can be used as a cost-effective source of feedback. We propose an approach to maximize the improvement of data integration systems given a budget for feedback. Our approach takes into account the annotation of schema matchings, mapping results and pairs of candidate record duplicates. We define a feedback plan, which indicates the type of feedback to collect, the amount of feedback to collect and the order in which different types of feedback are collected. We defined a fitness function and a genetic algorithm to search for the most cost-effective feedback plans. We implemented a framework to test the application of feedback plans and measure the improvement of different data integration systems. In the framework, we use a greedy algorithm for the selection of mappings. We designed quality measures to estimate the quality of a dataspace after the application of a feedback plan. For the evaluation of our approach, we propose a method to generate synthetic data scenarios. We evaluate our approach in scenarios with different characteristics. The results showed that the generated feedback plans achieved higher quality values than the randomly generated feedback plans in several scenarios.

This study attempts to shed light on the efficacy of crowdsourcing as a means of translating web content in Canada. Within, we seek to explore and understand if a model can be created that can estimate the effectiveness of crowdsourced translation as a means of bridging the Canadian Linguistic Digital Divide. To test our hypotheses and models, we use structural equation modeling techniques coupled with confidence intervals for comparing experimental crowdsourced translation to both professional and machine translation baselines. Furthermore, we explore a variety of factors which influence the quality of the experimental translations, how those translations performed in the context of their source text, and the ways in which the views of the quality of the experimental translations were measured before and after participants were made aware of how the experimental translations were created.

PERFORMANCE OF DEPENDENT WORK THROUGH DIGITAL PLATFORMS ABSTRACT This thesis discusses the issue of work performance through digital platforms, especially regarding whether it is possible to classify such activity as "dependent work", particularly with regard to the laws of the Czech Republic. The thesis responds to the current legal "vacuum" surrounding the performance of work through digital platforms and the legal status of persons who perform such activity (referred to in the thesis as workers). The mentioned legal "vacuum" stems primarily from the absence of sufficient legal regulation in the given area and the "unconventionality" of such a way of performing work. Digital platforms describe their workers as independent entrepreneurs, and this practice is quietly tolerated by the Czech Republic (e. g. administrative authorities). However, there can be more than reasonable doubts about the veracity of such proclamation. Therefore, the thesis firstly analyzes the characteristics and conditions of dependent work according to the laws of the Czech Republic, which are then applied to the reality of the performance of work through digital platforms. Due to the number of their types, as well as the various systems that the platforms implement, three representatives were chosen for the purposes of this thesis,...

Educational Digital Libraries (DLs) are complex information systems which are designed to support individuals' information needs and information seeking behavior. To have a broad impact on the communities in education and to serve for a long period, DLs need to structure and organize the resources in a way that facilitates the dissemination and the reuse of resources. Such a digital library should meet defined quality dimensions in the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework - including completeness, consistency, efficiency, extensibility, and reliability - to ensure that a good quality DL is built. In this research, we addressed both external and internal quality aspects of DLs. For internal qualities, we focused on completeness and consistency of the collection, catalog, and repository. We developed an application pipeline to acquire user-generated computing-related resources from YouTube and SlideShare for an educational DL. We applied machine learning techniques to transfer what we learned from the ACM Digital Library dataset. We built classifiers to catalog resources according to the ACM Computing Classification System from the two new domains that were evaluated using Amazon Mechanical Turk. For external qualities, we focused on efficiency, scalability, and reliability in DL services. We proposed cloud-based designs and applications to ensure and improve these qualities in DL services using cloud computing. The experimental results show that our proposed methods are promising for enhancing and enriching an educational digital library. This work received support from ACM, as well as the National Science Foundation under Grant Numbers DUE-0836940, DUE-0937863, and DUE-0840719, and IMLS LG-71-16-0037-16. / Ph. D.

Essenslieferungen, Fahrdienste oder etwa die Erledigung von Kleinstaufträgen sog. „Microjobs“, besonders seit der COVID-19-Pandemie erfolgt diese Arbeit immer häufiger unter Vermittlung digitaler Plattformen. Diese Untersuchung eröffnet den Blick auf eine in der Vergangenheit gänzlich unbekannte Form der Arbeitsorganisation, bei der einer vermeintlichen Autonomie der Plattformarbeiter ein Intermediär gegenübersteht, der seine zentrale Position zur Steuerung und Kontrolle ebendieser Plattformarbeiter nutzt und dennoch das Vorliegen eines Arbeitsverhältnisses in der Regel vehement bestreitet. Die Dissertation arbeitet zunächst die theoretischen Grundlagen und wirtschaftlichen Hintergründe derartiger Plattformarbeit heraus. Im zweiten Schritt erfolgt auf Basis von Fallbeispielen – Clickworker, Deliveroo, Upwork – erstmals eine umfassende steuerrechtliche Einordnung des Phänomens Plattformarbeit. Hierbei werden die wesentlichen Besonderheiten im Vergleich zu tradierten Arbeitsverhältnissen, insbesondere die Steuerung der Plattformarbeiter mittels algorithmusbasierter Methoden der Verhaltensökonomie, und deren Auswirkungen auf die steuerliche Einordnung ausführlich beleuchtet. Es kann nachgewiesen werden, dass abhängig von der Art der zu erledigenden Aufgaben vom Intermediär ein Anreizsystem geschaffen werden muss, welches eine indirekte Steuerung des Plattformarbeiters zum Ziel und den Bezug von Einkünften aus nichtselbständiger Arbeit zur Folge hat. Abschließend werden für die ermittelten Unzulänglichkeiten der tradierten steuerlichen Abgrenzungskriterien Lösungsvorschläge entwickelt, etwa eine Beweislastregelung zu Gunsten der Plattformarbeiter. Auf Grund des Querschnittcharakters des Themas schafft die Arbeit zugleich interessante Ansatzpunkte für andere Rechtsgebiete, beispielsweise das Arbeits- oder Vertragsrecht. Stand des Werkes ist Juli 2020.

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