Refine
Year of publication
- 2016 (56) (remove)
Document Type
- Article (13)
- Bachelor Thesis (13)
- Conference Proceeding (8)
- Report (8)
- Study Thesis (6)
- Working Paper (5)
- Book (1)
- Master's Thesis (1)
- Periodical Part (1)
Has Fulltext
- yes (56)
Is part of the Bibliography
- no (56)
Keywords
- Autobewerter (3)
- E-Learning (3)
- Grader (3)
- IT-Sicherheit (3)
- Java <Programmiersprache> (3)
- Programmieraufgabe (3)
- Programmierung (3)
- e-Assessment (3)
- Öffentliche Bibliothek (3)
- Übung <Hochschule> (3)
Research question: Rivalries in team sports are commonly conceptualized as a threat to the fans’ identity. Therefore, past research has mainly focused on the negative consequences. However, theoretical arguments and empirical evidence suggest that rivalry has both negative and positive effects on fans’ self-concept. This research develops and empirically tests a model which captures and integrates these dual effects of rivalry.
Research methods: Data were collected via an on-site survey at home games of eight German Bundesliga football teams (N = 571). Structural equation modeling provides strong support for the proposed model.
Results and findings: In line with previous research, the results show that rivalry threatens fans’ identity as reflected in lower public collective self-esteem in relation to supporters of the rival team. However, the results also show that there are crucial positive consequences, such as higher perceptions of public collective self-esteem in relation to supporters of non-rival opponents, perceived ingroup distinctiveness and ingroup cohesion. These positive effects are mediated through increases in disidentification with the rival and perceived reciprocity of rivalry.
Implications: We contribute to the literature by providing a more balanced view of one of team sports’ key phenomena. Our results indicate that the prevalent conceptualization of rivalry as an identity threat should be amended by the positive consequences. Our research also offers guidance for the promotion of rivalries, where the managerial focus should be on creating a perception that a rivalry is reciprocal.
Diese Literaturrecherche versucht eine Darstellung des Leseprozesses sowie der Leichten Sprache, die Menschen mit Lesestörungen dienen soll.
Es stellt sich heraus, dass künftig ein anderes System erforderlich sein wird, das es gestattet, Leserkategorien und Lernprozesse zu berücksichtigen.
Die Entwicklung einer Alternative auf der Basis einer geregelten Sprache (controlled language) könnte im Unterschied zur Leichten Sprache Dokumenttypen, Leserkategorien, Wort- und Grammatikkenntnisse sowie das beim Leser vorhandene Wissen berücksichtigen.
In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.
Integrating distributional and lexical information for semantic classification of words using MRMF
(2016)
Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
One of the main concerns of this publication is to furnish a more rational basis for discussing bioplastics and use fact-based arguments in the public discourse. Furthermore, “Biopolymers – facts and statistics” aims to provide specific, qualified answers easily and quickly for decision-makers in particular from public administration and the industrial sector. Therefore, this publication is made up like a set of rules and standards and largely foregoes textual detail. It offers extensive market-relevant and technical facts presented in graphs and charts, which means that the information is much easier to grasp. The reader can expect comparative market figures for various materials, regions, applications, process routes, agricultural land use, water use or resource consumption, production capacities, geographic distribution, etc.