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To conclude this chapter, I would like to focus on an aspect which has already been highlighted in the introductory chapter of this thesis and which demonstrates considerable relevance to both scientific and technical translation in general and to the various epistemic aims of the present thesis. This aspect goes by the name of linguistic underdeterminacy

(e.g. Carston 2002:19, see also chapter 1) and is the theoretical label for the trivial but nevertheless important fact that, in human communication, we generally understand more than the actual words uttered or written (see also Hörmann 1976:210). Put another way, we need to bring additional knowledge to underdetermined or impoverished linguistic structures if we want to give them a coherent interpretation. As Carston (2002:19) rightly points out, the idea of linguistic underdeterminacy is widely accepted and not seriously disputed by anyone but perhaps the “most rabid ‘language is all’ social semiotician[s]”. A metaphor which is often applied in this context is the iceberg metaphor according to which textual surface structures are only the tip of the iceberg in meaning construction, the larger part of this iceberg being hidden under the surface (Linke/Nussbaumer 2000:435; Prunč

2007:21). A second popular metaphor is the rhetorical figure of synecdoche in its pars pro toto version, expressing the fact that the textual surface structures represents merely a part of a larger whole, this whole being the actual sense or meaning of the text (Lederer 2003:52-53, 2010:176-177). For the time being, the notion of linguistic underdeterminacy will be discussed from a general and not from a specific theoretical perspective and the focus will be on some of its consequences for STT. As a recurring theme of the thesis, the concept will then be taken up again in the context of cognitive linguistics and explicitation and implicitation, thereby being continuously theoretically enriched.

At the most general level, linguistic underdeterminacy entails that translators of scientific and technical discourse need to acquire a reasonable amount of – strongly vertically organized – domain knowledge in order to arrive at a coherent interpretation of the source text based on which they create a target text (see 2.4.1). The issue at stake is described very clearly in this quote from Faber Benítez (2009:108), in which we encounter one of the metaphors illustrated previously:

The information in scientific and technical texts is encoded in terms or specialized knowledge units, which can be regarded as access points to more complex knowledge structures. As such, they only mark the tip of the iceberg. Beneath the waters stretch the tentacles of a many-splendored conceptual domain, which represents the implicit knowledge underlying the information in the text.

The notion of linguistic underdeterminacy can be incorporated in a straightforward way into the classification of scientific and technical texts proposed above. Symmetrical communicative situations in expert-to-expert discourse will probably be characterized by a very high degree of linguistic underdeterminacy (for example in the form of ellipses or lexical or syntactic compression, see Fijas 1998:393; Hoffmann 1998:421) since the discourse participants share a large amount of relevant knowledge that does not have to be explicitly verbalized in communication, thus underlying the information in the text in implicit form. This follows from Grice’s (1975) cooperative principle and especially from his maxim of relation, according to which one should not make one’s contribution more informative than required.35

In other words, then, the hidden part of the communicative iceberg tends to be quite large in expert-to-expert communication. On the other hand, in expert-to-semi-expert and expert- to-layperson discourse, the degree of linguistic underdeterminacy and hence the invisible part of the iceberg generally decreases since there is a decreasing amount of relevant shared knowledge between the discourse participants and, accordingly, more and more information has to be linguistically encoded to ensure the understanding of the text.

The generally high degree of linguistic underdeterminacy in expert-to-expert discourse is also linked to the high degree of technicality exhibited by texts belonging to this form of discourse. As we can see from Arntz’ scale above, the specialized knowledge required to understand scientific and technical texts becomes more extensive as the degree of technicality of these texts increases. This knowledge is precisely that knowledge which is shared between experts in a given field and can thus remain largely implicit in their communication. This insight is in line with Krein-Kühle’s (2003:11) observation in the context of STT that “the higher the degrees of specialization and abstraction, the lesser the clarity for the translator”.

36

35 _{A related concept would be Clark’s (1992:201-202) notion of audience design.}

36 _{Of course, there is a level at which expert-to-expert discourse will be less underdetermined or more}

specific than expert-to-semi-expert and expert-to-layperson discourse. This level pertains to the actual states of affairs discussed, which will certainly be more specific than in the other forms of discourse. For example, the term low capacitance small-area silicon diode detector (Arntz 2001:202) is in a way more specific or less