e-university: a cross-case study in four Italian universities

This paper describes the aims, the methods, the stages and the results of the research project 'e-University. Information and communication technologies (ICTs) as a factor of change in Italian universities', undertaken between 2003 and 2006 by the Universities of Trento, Perugia and Sassari and the Catholic University of Milan.
The characteristics of the four institutions' subjects and objects of the investigation are analysed as case studies and then compared as a cross-case study in the light of the factors that we feel constitute the main areas of concern in the four institutions as to the implementation of ICTs in the teaching/learning process, ie, the characteristics of the governance strategic plan, the choices relating to technology and the choices related to pedagogy. The paper describes the collaborative effort characterising the community of researchers through the different stages of the research. It also analyses how the individual features of each university—relating to its history, the number of students, the range of faculties, the diffusion of e-learning, the driving factors for innovation, the institution's policies—have fostered a lively and fruitful discussion between different view points, thus engendering deeper reflections for each context.     

Universities’ attractiveness to students: The Darwinism effect

Due to significant government cuts to Higher Education funding in Southern European systems, their already underfunded universities were forced to increasingly compete for students as sources of additional revenue. Concurrently, families and students that continued to afford participation in Higher Education became more selective when choosing a university, realising the riskier investment that Higher Education participation had become. Through a competing destinations model and relying on all Italian private and public universities, this study finds that the competition forces characterising universities’ attractiveness over the last decade have changed since the financial crisis of 2008. In a context of lower demand for Higher Education, the competition for students grew and universities in close proximity were better prepared to face the new challenges, leading to the growth of Higher Education clusters.     

Detecting ethnicity-targeted hate speech in Russian social media texts

Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore, ethnicity-targeted hate speech detection in user texts is becoming an important task. However, it faces a number of unresolved problems: difficulties of reliable mark-up, informal and indirect ways of expressing negativity in user texts (such as irony, false generalization and attribution of unfavored actions to targeted groups), users’ inclination to express opposite attitudes to different ethnic groups in the same text and, finally, lack of research on languages other than English. In this work we address several of these problems in the task of ethnicity-targeted hate speech detection in Russian-language social media texts. This approach allows us to differentiate between attitudes towards different ethnic groups mentioned in the same text – a task that has never been addressed before. We use a dataset of over 2,6M user messages mentioning ethnic groups to construct a representative sample of 12K instances (ethnic group, text) that are further thoroughly annotated via a special procedure. In contrast to many previous collections that usually comprise extreme cases of toxic speech, representativity of our sample secures a realistic and, therefore, much higher proportion of subtle negativity which additionally complicates its automatic detection. We then experiment with four types of machine learning models, from traditional classifiers such as SVM to deep learning approaches, notably the recently introduced BERT architecture, and interpret their predictions in terms of various linguistic phenomena. In addition to hate speech detection with a text-level two-class approach (hate, no hate), we also justify and implement a unique instance-based three-class approach (positive, neutral, negative attitude, the latter implying hate speech). Our best results are achieved by using fine-tuned and pre-trained RuBERT combined with linguistic features, with F1-hate=0.760, F1-macro=0.833 on the text-level two-class problem comparable to previous studies, and F1-hate=0.813, F1-macro=0.824 on our unique instance-based three-class hate speech detection task. Finally, we perform error analysis, and it reveals that further improvement could be achieved by accounting for complex and creative language issues more accurately, i.e., by detecting irony and unconventional forms of obscene lexicon.     

It's never too late: Funding dynamics and self pledges in reward-based crowdfunding

Crowdfunding recently emerged as an alternative funding channel for entrepreneurs. We use pledge-level data from Startnext, the biggest German platform, to gain insights on funding dynamics and pledgers’ motivations. We find that the majority of projects that eventually succeed are not on a successful track at 75% of their funding period. These late successes are boosted by information cascades during the final 25% of the funding duration. We conclude – in contrast with earlier literature – that project success is only partially path-dependent. While early pledges do anticipate project success, a lack of them does not necessarily mean that projects will fail. Interviews and questionnaire responses indicate that projects’ communication efforts play a role in making severely under track projects succeed eventually. Moreover, our dataset uniquely allows us to quantify the extent of self funding. Self pledges account for about 10% of all initial pledges and 9% of all pledges that secure funding. Nonetheless, the late surges at severely under track projects are mostly driven by external funders. Furthermore, we find no evidence of subsequent herding triggered by self pledges.     

意大利和国际体育法及判例制裁的比例原则

研究意大利和国际体育法判例制裁的比例原则,首先需要了解意大利(以下简称国家)和国际体育规则。其次是根据意大利与国际体育法的判例如何与归责原则连接,以及解释(和执行)方式,尤其是关于足球与反兴奋剂规则。研究的主要目的是为了验证这一原则是否在规则中被编撰和认可(通过体育法庭得到解释和执行),评估这一原则是否应用于保护体育运动的完整性(确保它的正常运作),并应当平衡要遵守体育规则和保护运动员利益二者之间的关系。     

The contribution of urban-scale environmental monitoring to materials diagnostics: A study on the Cathedral of Modena (Italy)

The problem of environmental monitoring aimed at identifying and evaluating the weathering mechanisms affecting historical stoneworks is here discussed and a methodology based on the use of already available, long record, urban-scale environmental data is proposed. An example of its application to the Cathedral of Modena (Italy, XII-XIV century, included in the UNESCO's World Heritage List) is then presented. In this case, the methodology allowed the identification of the main degradation causes, which found significant confirmation by material diagnostics on the available samples and finite element structural modelling. Thanks to the decay causes identification allowed by the proposed methodology, more effective restoration procedures can be outlined.