Abstract
We began this book with an anecdote about how global knowledge dynamics have traditionally worked through trade routes. Today, global knowledge dynamics are reworked through digital modes of engagement that influence which and how people form ties with each other, as well as which knowledge diffuses in which ways. This final chapter recaps the central insights of the book, and takes another look at the potentials and challenges that arise from today’s computational-assisted complex cultural networks.
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Notes
- 1.
These languages include English (54 per cent), Russian (6 per cent), German (6 per cent), Japanese (5 per cent), Spanish (5 per cent), French (4 per cent) Portuguese (2.5 per cent), Italian (2 per cent), Chinese (2 per cent), and Polish (2 per cent) (we3techs.com, 2016). Cf. http://w3techs.com/technologies/overview/content_language/all (retrieved 1 May 2016).
- 2.
However, once such cultural sensitivities are respected combinability drives novel arrangements. To take another example from indigenous people: research on the Google Translator Toolkit by Māori language specialist Te Taka Keegan has shown that it helps smaller languages by providing knowledge resources to unify the language’s written form or increase translation speed and quality of documents published in that language (Helft, 2010).
- 3.
Of course, entrepreneurs not only base decisions in favour of noncombinability on key investment indicators such as ROI. This becomes clear when we consider the following example. For a long time, Google Translate and Google Search were persistently interdependent in that any translation was recognised by the search engine as acceptable, thereby affecting ranking relevancy for search results. This leverage also was noted and acted on at large scale by various users. The result was that websites with poorly translated content ranked so prominently in search engine results that Google regarded it as a serious threat to its business model. What happened? Realising the leverage that the worldwide demand for content localisation would provide for both its search engine and translation model, Google introduced a range of translation services to help people access and make available more content, as well as to feed Google’s translation model with more data to improve translation quality. One of these services was the Google Translate API. The Translate API was designed to allow websites and programs to integrate with Google Translate. Google released the Translate API free of charge, and it has been used extensively by a wide array of web developers, web publishers, and applications to create multilingual content. In late May 2011, however, after noticing that some users were gaming the system (i.e., the translate-search-leverage) Google announced that the popular API would be terminated because of substantial economic burden.
References
Helft, M. (2010). Google’s Toolkit for Translators Helps Feed Its Machine. The New York Times, March 9. Available at http://bits.blogs.nytimes.com/2010/03/09/googles-toolkit-for-translators-helps-feed-its-machine/ (retrieved 30 March 2010).
Hidalgo, C. (2014). Why Information Grows. New York: Basic Books.
w3techs.com. (2016). Usage of content languages for websites. Available at https://w3techs.com/technologies/overview/content_language/all (retrieved 1 May 2016).
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Petzold, T. (2017). Complexity and Simplicity. In: Global Knowledge Dynamics and Social Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-41234-4_8
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DOI: https://doi.org/10.1007/978-3-319-41234-4_8
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