2020
DOI: 10.1080/14708477.2020.1812621
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Transcultural literacies in online collaboration: a case study of fanfiction translation from Russian into English

Abstract: This study analyses the transcultural meaning-making and literacies that arise during the amateur translation of a fanfiction novel from Russian to English. We applied digital ethnography to make observations of the translation process and interviews with the participants. Transcultural meaning-making was identified during their discussions on how to adapt a Russian fanfiction novel for a global English-speaking readership. During the discussions the participants creatively mixed different linguistic and cultu… Show more

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Cited by 13 publications
(6 citation statements)
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References 30 publications
(51 reference statements)
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“…Literature [13] chooses to wait for the current data stream to be processed before routing the new data stream to process it, but this increases the time and cost of updating. Literature [14] replicates and transfers the state of network functions in a fine-grained manner to ensure consistent properties during the update process. At the same time, in order to avoid network performance degradation, they will cache data packets in the controller during the update process.…”
Section: Related Workmentioning
confidence: 99%
“…Literature [13] chooses to wait for the current data stream to be processed before routing the new data stream to process it, but this increases the time and cost of updating. Literature [14] replicates and transfers the state of network functions in a fine-grained manner to ensure consistent properties during the update process. At the same time, in order to avoid network performance degradation, they will cache data packets in the controller during the update process.…”
Section: Related Workmentioning
confidence: 99%
“…The modulo document is the product of the conditional probabilities of all words, and each conditional probability is generated using a feedforward neural network to obtain object-level topic features with high-level semantic information. In the literature [13], the DocNADE model was introduced into the field of translation understanding, and it was proposed to do translation classification and annotation based on the Doc-NADE model, and it also achieved good performance. The translation understanding work based on deep learning has achieved fruitful results, shortening the translation semantic gap to a certain extent, but there is still some room for improvement.…”
Section: Related Workmentioning
confidence: 99%
“…All the participants are native speakers of Russian who live in post-Soviet countries and use Russian as their lingua franca (more about the language use in Shafirova, Cassany & Bach, 2020). The participants did not know each other before the beginning of the translation project.…”
Section: Main Participants' Descriptionmentioning
confidence: 99%