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Translation and Migration" examines a wide range of written, spoken, visual and online exchanges that occur within multicultural societies, involving some form of translation. Topics covered include translations in public spaces, such as signs and announcements; the role of translation in transnational media- covering radio, television, advertising and the internet; and the part that translators and interpreters play in legal, health and human services settings. This is key reading for students undertaking Translation Studies courses, and will also be of interest to researchers in sociology, cultural studies, anthropology and migration studies

This paper explores the use of general-purpose machine translation (MT) in assisting the users of computer-aided translation (CAT) systems based on translation memory (TM) to identify the target words in the translation proposals that need to be changed (either replaced or removed) or kept unedited, a task we term as "word-keeping recommendation". MT is used as a black box to align source and target sub-segments on the fly in the translation units (TUs) suggested to the user. Source-language (SL) and target-language (TL) segments in the matching TUs are segmented into overlapping sub-segments of variable length and machine-translated into the TL and the SL, respectively. The bilingual sub-segments obtained and the matching between the SL segment in the TU and the segment to be translated are employed to build the features that are then used by a binary classifier to determine the target words to be changed and those to be kept unedited. In this approach, MT results are never presented to the translator. Two approaches are presented in this work: one using a word-keeping recommendation system which can be trained on the TM used with the CAT system, and a more basic approach which does not require any training. Experiments are conducted by simulating the translation of texts in several language pairs with corpora belonging to different domains and using three different MT systems. We compare the performance obtained to that of previous works that have used statistical word alignment for word-keeping recommendation, and show that the MT-based approaches presented in this paper are more accurate in most scenarios. In particular, our results confirm that the MT-based approaches are better than the alignment-based approach when using models trained on out-of-domain TMs. Additional experiments were performed to check how dependent the MT-based recommender is on the language pair and MT system used for training. These experiments confirm a high degree of reusability of the recommendation models across various MT systems, but a low level of reusability across language pairs. ; This work is supported by the Spanish government through projects TIN2009-14009-C02-01 and TIN2012-32615.

This article confronts the basic idea of securitization with the concept of translation. By critically examining Wæver's deliberately traditionalist and essentialist conceptualization of security and his notion of a distinctly speech-act-theoretical approach to securitization, it develops a processual refinement that reads articulations of security as translations. I claim that this conceptual transposition has the potential to open the current securitization discourse to an alternative perspective and to new avenues of research on the travel, localization and/or gradual evolution/transformation of security meanings.

Intro -- Chapter One -- Chapter Two -- Chapter Three -- Chapter Four -- Chapter Five -- Chapter Six -- Chapter Seven -- Chapter Eight -- Chapter Nine -- Chapter Ten -- Chapter Eleven -- Chapter Twelve -- Chapter Thirteen -- Chapter Fourteen -- Chapter Fifteen -- Chapter Sixteen -- Chapter Eighteen -- Acknowledgements.