853 resultados para Deterrence and cooperation


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Universities face constant scrutiny about their plagiarism management strategies, policies and procedures. A resounding theme, usually media inspired, is that plagiarism is rife, unstoppable and university processes are ineffectual in its wake. This has been referred to as a 'moral panic' approach (Carroll & Sutherland-Smith, forthcoming; Clegg, 2007) and suggests plagiarism will thwart all efforts to reclaim academic integrity in higher education. However, revisiting the origins of plagiarism and exploring its legal evolution reveals that legal discourse is the foundation for many plagiarism management policies and processes around the world. Interestingly, criminal justice aims are also reflected in university plagiarism management strategies. Although universities strive for deterrence of plagiarism in a variety of ways, the media most often calls for retribution through increasingly tougher penalties. However, a primary aim of the justice system, sustainable reform, is not often reported in the media or visible in university policies or processes. Using critical discourse analysis, this paper examines the disjunction between media calls for increased retribution in the wake of moral panic and institutional responses to plagiarism. I argue that many universities have not yet moved to sustainable reform in plagiarism management.

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While the United States is an important Asia-Pacific actor, its engagement with the region is complex and often difficult. Not only must US regionalism balance the diverse requirements of an ambitious policy agenda, but also US policy norms and priorities often clash with those of other regional actors. This has important implications for the capacity of the United States to provide regional leadership. Recent years have seen growing policy convergence between the United States and other Asia-Pacific actors, particularly in economic terms, but US regionalism continues to feature competition alongside collaboration.

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Settlements are an important part of a program of cartel deterrence, particularly when the likelihood of conviction and the litigation costs are higher. This type of negotiated procedure to reach finality is in essence complementary to the fully adversarial procedures associated to the trial by the administrative or judicial courts, and to other investigative instruments, such as the leniency agreement. The Brazilian experience provides some insights about the different models of direct settlement in cartel cases and the complex interaction among settlements, leniency agreements, and trial outcome. First, there is leeway for the complementary models of settlements, the first oriented mainly to increasing the likelihood of detection, and the second oriented to saving social costs of litigation. Second, the concern with the preservation of the demand for leniency agreements led the competition authority to restrict the use of settlements, which are effectively designed for the defendants that are likely guilty and give higher value to finality. The recent experience illustrates that the current settlement policy has not caused any adverse effect on leniency agreements, while reducing litigation costs and granting finality in some cases.

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This article examines the arising cross-border dispute resolution models (Cooperation and Competition among national Courts) from a critical perspective. Although they have been conceived to surpass the ordinary solution of a Modern paradigm (exclusive jurisdiction, choice of court, lis pendens, forum non conveniens, among others), they are insufficient to deal with problems raised with present globalization, as they do not abandon aspects of that paradigm, namely, (i) statebased Law; and (ii) standardization of cultural issues.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

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Includes bibliography