Abstract
Many of Fishman’s contributions to understanding language in society stress the importance of dynamics, drawing attention to the complex interplay of micro-, meso- and macro-level factors from which an integrated pattern emerges. Our understanding of language dynamics, therefore, should encompass processes unfolding at various levels and provide accounts that do justice to these interactions, while delivering an analysis broad enough to constitute a sensible basis for successful language policy. Such concerns, illustrated in particular by Fishman’s work on reversing language shift, call for revisiting this issue by focusing on the role of translation. Translation is linked to language dynamics, and it is both a conduit of language policies and a condition for their success, but these interconnections need to be explicitly acknowledged. Whereas translation studies often approach translation itself as a self-contained process, it certainly emerges from multilingual contexts, but is also, at least in part, dependent on language policies. Translation contributes to the maintenance of linguistic diversity and societal multilingualism which are, reciprocally, dependent upon the practice of translation. This examination confirms the ongoing soundness of the fundamentals of Fishman’s approach to “language-in-society” and helps to assess some recent criticism toward core notions of classical sociolinguistics that Fishman helped develop and disseminate, such as multilingualism, which is being called into question by current notions such as “English as a lingua franca” and “languaging”. The very existence of translation as a social, economic and political practice suggests that societal multilingualism cannot satisfactorily be described without resorting to classical sociolinguistic concepts like “named” languages, mother tongue and domain, which are crucial to successful policies and, hence, to the maintenance of the linguistic human rights to which Fishman’s work has made such essential contributions.
Acknowledgments
Earlier versions of this article have been presented at various workshops, in particular the Translation Forum of the European Commission’s Directorate General for Translation in March 2010. The author wishes to thank Marco Civico for his very dependable research assistance, and Gilles Falquet, Michele Gazzola, Mathieu Guidère, François Vaillancourt, as well as two anonymous referees for insightful suggestions and comments. The usual disclaimer applies.
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Appendix: Measuring multilingualism in communication
Proposing a metric for diversity based on the relative share of communication taking place in different languages raises the challenge of operationalizing this notion, at least in principle.
If “utterances” in different languages are used as the basic unit of measurement of multilingualism, the number of utterances needs to be adjusted to take account of the number of recipients (listeners, readers, etc.). As a first approximation, we may assume that if a speaker (or writer) addresses an audience of, say, one thousand, then this instance of communication should count as one thousand in our reckoning, whereas if the speaker had addressed only one listener, this same instance would have counted as one.
Suppose that a total of K utterances is made in language j. Each utterance reaches a specific number of recipients Rj,k, where k=1, 2, …, K. Then the share of language j in total communication is sj=(∑k Rj,k)/R, where R is the total number of receivers of all messages uttered in all languages. Clearly, persons will be counted more than once in R, since they normally receive more than one message during any observation period.
This definition raises one problem, namely, that of knowing the audience size for each utterance. However, this information may be replaced by an approximation. What justifies using one is the fact that it is probably more realistic to assume that in terms of resulting aggregate diversity, the importance of the marginal listener, for each individual utterance, is positive but decreasing. Thus, we would be led to pick an appropriate logarithmic-type transformation of Rj,k for each individual utterance (oral or written) in language j.
One possibility is to call on Zipf’s law, which applies not only, as in its well-known initial formulation, to the relationship between the frequency and rank of words in natural languages (a constant according to Zipf), but to a host of other phenomena, from the rank-size distribution of cities in any given country to access to Internet pages (Adamic and Huberman 2002). This latter result is particularly relevant to communication: for example, if the most frequently consulted Internet page has been accessed t times, the second most frequently read will be accessed t/2 times, the third t/3 times, and so on. Thus, Pj Internet pages in language j give rise to a total number of “messages” Mj=Rj×(1+1/2+1/3+…+1/Pj), where Rj is the number of times the most frequently consulted j-language page has been accessed. Moving to the continuous case, the term Mj can be re-expressed as:
If the Zipf law pattern holds more generally, it can be used as an approximation of the actual number of “effective utterances”, for which we only need to know the (approximate) number of recipients reached by the most successful utterance. A fraction f of the total number of speakers of a language (above, say, the age of 4) can provide a reasonable estimate of Rj (meaning that a share f of the j-speaking population will be reached by the most successful of all the messages uttered in language j, whether this message is a political speech, a news broadcast or a commercial ad). The number of different utterances in language j, Pj, can be approached as a multiple of the total number of speakers, with some speakers emitting a large number of oral and written messages, and others very few. For the purposes of estimating Pj, the definition of a “speaker” need not be restricted to physical persons but can extend to administrations, media channels and firms – whoever, in fact, can emit messages.
Calculating Mj for each of the N languages present in a given context like a neighbourhood, city or country, we can compute M as the sum of all Mj’s for j=1, 2, … N, namely, M=∑j Rj×ln(Pj). The linguistic evenness of the context considered can then be expressed by replacing the term sj appearing in eq. [1] in the text by (Mj/M)2.
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