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A tale of many stories: explaining policy diffusion between European higher education systems
(2013)
The thesis ”A Tale of Many Stories - Explaining Policy Diffusion between European Higher Education Systems" systematically examines diffusion processes and their effects with regard to a rather neglected policy area – the case of European higher education policy. The thesis contributes to the slowly growing number of comparative and mechanism-based studies on policy diffusion and represents the first study on the diffusion of policies between European Higher Education Systems. The main aim is to contrast and compare testable and coherent explanatory models on the functioning of different diffusion mechanisms. Three sets of explanatory models on the relationship between variables triggering and conditioning diffusion mechanisms and their impact on policy adoption are drawn from mechanism-based thinking on policy diffusion: on learning, socialization, and externalities. These approaches conceptualize the policy process in terms of interdependencies between international and national actors. Explanatory models based on assumptions about domestic policies and the common responses of countries to similar policy problems extend this theoretical framework. The thesis is based on event history modelling of policy change and adoption in higher education systems of 16 West European countries between the yeas 1980 and 1998. Overall 14 policy items describing performance-orientated reforms for public universities ranging from the adoption of external quality assurance systems to tuition fees are examined. Empirically, the main research question is what international, national and policy-specific factors cause and condition diffusion processes and the adoption of public policies? Evidence can be found for and against all of the four theoretical approaches tested. In comparison, many of the assumptions related to interdependencies lack robustness, whereas the common response model is the most stable one. This does not mean that explanatory models based on interdependent decision-making are not suitable for analysing policy diffusion in higher education. Rather interdependency is a multi- dimensional concept that requires a comparative assessment of diffusion mechanisms. Some of explanatory factors based on interdependent decision- making are still supported by the empirical analysis though. From this point of view, the recommendation for analysing diffusion is to start with a model based on domestic politics, that is successively extended by explanatory factors dealing with interdependencies between international and national actors. Diffusion variables matter – but it is only one side of the tale on policy diffusion.
In the rapidly changing circumstances of our increasingly digital world, reading is also becoming an increasingly digital experience: electronic books (e-books) are now outselling print books in the United States and the United Kingdom. Nevertheless, many readers still view e-books as less readable than print books. The present study thus used combined EEG and eyetracking measures in order to test whether reading from digital media requires higher cognitive effort than reading conventional books. Young and elderly adults read short texts on three different reading devices: a paper page, an e-reader and a tablet computer and answered comprehension questions about them while their eye movements and EEG were recorded. The results of a debriefing questionnaire replicated previous findings in that participants overwhelmingly chose the paper page over the two electronic devices as their preferred reading medium. Online measures, by contrast, showed shorter mean fixation durations and lower EEG theta band voltage density – known to covary with memory encoding and retrieval – for the older adults when reading from a tablet computer in comparison to the other two devices. Young adults showed comparable fixation durations and theta activity for all three devices. Comprehension accuracy did not differ across the three media for either group. We argue that these results can be explained in terms of the better text discriminability (higher contrast) produced by the backlit display of the tablet computer. Contrast sensitivity decreases with age and degraded contrast conditions lead to longer reading times, thus supporting the conclusion that older readers may benefit particularly from the enhanced contrast of the tablet. Our findings thus indicate that people’s subjective evaluation of digital reading media must be dissociated from the cognitive and neural effort expended in online information processing while reading from such devices.
We investigate the task of detecting reliable statements about food-health relationships from natural language texts. For that purpose, we created a specially annotated web corpus from forum entries discussing the healthiness of certain food items. We examine a set of task-specific features (mostly) based on linguistic insights that are instrumental in finding utterances that are commonly perceived as reliable. These features are incorporated in a supervised classifier and compared against standard features that are widely used for various tasks in natural language processing, such as bag of words, part-of speech and syntactic parse information.
Interested in formally modelling similarity between narratives, we investigate judgements of similarity between narratives in a small corpus of film reviews and book–film comparisons. A main finding is that judgements tend to concern multiple levels of story representation at once. As these texts are pragmatically related to reception contexts, we find many references to reception quality and optimality. We conclude that current formal models of narrative can not capture the task of naturalistic narrative comparisons given in the analysed reviews, but that the development of models containing a more reception-oriented point of view will be necessary.
The understanding of story variation, whether motivated by cultural currents or other factors, is important for applications of formal models of narrative such as story generation or story retrieval. We present the first stage of an experiment to elicit natural narrative variation data suitable for evaluation with respect to story similarity, to qualitative and quantitative analysis of story variation, and also for data processing. We also present few preliminary results from the first stage of the experiment, using Red Riding Hood and Romeo and Juliet as base texts.
Linguistic query systems are special purpose IR applications. We present a novel state-of-the-art approach for the efficient exploitation of very large linguistic corpora, combining the advantages of relational database management systems (RDBMS) with the functional MapReduce programming model. Our implementation uses the German DEREKO reference corpus with multi-layer
linguistic annotations and several types of text-specific metadata, but the proposed strategy is language-independent and adaptable to large-scale multilingual corpora.
We examine predicative adjectives as an unsupervised criterion to extract subjective adjectives. We do not only compare this criterion with a weakly supervised extraction method but also with gradable adjectives, i.e. another highly subjective subset of adjectives that can be extracted in an unsupervised fashion. In order to prove the robustness of this extraction method, we will evaluate the extraction with the help of two different state-of-the-art sentiment lexicons (as a gold standard).