Reacting and responding in TESOL Quarterly:a generic analysis of procedure and purpose in intertextual critical exchanges

A key feature of ‘TESOL Quarterly’, a leading journal in the world of TESOL/applied linguistics, is its ‘Forum’ section which invites ‘responses and rebuttals’ from readers to any of its articles. These ‘responses or rebuttals’ form the focus of this research. In the interchanges between readers reacting to earlier research articles in TESOL Quarterly and authors responding to the said reaction I – examine the texts for evidence of genre-driven structure, whether shared between both ‘reaction’ and ‘response’ sections, or peculiar to each section, and attempt to determine the precise nature of the intended communicative purpose in particular and the implications for academic debate in general. The intended contribution of this thesis is to provide an analysis of how authors of research articles and their critics pursue their efforts beyond the research article which precipitated these exchanges in order to be recognized by their discourse community as, in the terminology of Swales (1981:51), ‘Primary Knowers’. Awareness of any principled generic process identified in this thesis may be of significance to practitioners in the applied linguistics community in their quest to establish academic reputation and in their pursuit of professional development. These findings may also be of use in triggering productive community discussion as a result of the questions they raise concerning the present nature of academic debate. Looking beyond the construction and status of the texts themselves, I inquire into the kind of ideational and social organization such exchanges keep in place and examine an alternative view of interaction. This study breaks new ground in two major ways. To the best of my knowledge, it is the first exploration of a bipartite, intertextual structure laying claim to genre status. Secondly, in its recourse to the comments of the writers’ themselves rather than relying exclusively on the evidence of their texts, as is the case with most studies of genre, this thesis offers an expanded opportunity to discuss perhaps the most interesting aspects of genre analysis – the light it throws on social ends and the role of genre in determining the nature of current academic debate as it here emerges.

Decoherence of charge qubit coupled to interacting background charges

The major contribution to decoherence of a double quantum dot or a Josephson-junction charge qubit comes from the electrostatic coupling to fluctuating background charges hybridized with the conduction electrons in the reservoir. However, estimations according to previously developed theories show that finding a sufficient number of effective fluctuators in a realistic experimental layout is quite improbable. We show that this paradox is resolved by allowing for a short-range Coulomb interaction of the fluctuators with the electrons in the reservoir. This dramatically enhances both the number of effective fluctuators and their contribution to decoherence, resulting in the most dangerous decoherence mechanism for charge qubits.

Infer user interests via link structure regularization

Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification. © 2014 ACM.