Poetry Translating as Expert Action Processes, priorities and networks

(Amelia) #1

 Poetry Translating as Expert Action


Figure 26. Toen wij: Version 1 (Fleur, Lines 6–7)


Pre-analysis confirmed that translators manage their work across a poem’s ‘trans-
lating lifetime’, from their first reading of the source until they feel the translation
is finally ready, by dividing it into nesting ‘levels’:


  1. Draft sessions, separated by drawer time. Here, three were recorded (coded
    Draft1, etc.).

  2. Typically, each draft produces one or more separate written versions of the
    target text (see Figure 26). Especially when word-processed, however, a single
    version may be repeatedly re-edited, even across several drafts.

  3. Typically, each version is produced via one or more strategic runs-through:
    passes through the poem from beginning to end (numbered by count within
    Draft, e.g. Draft2/RT1, Draft2/RT2).

  4. A run-through involves one or more strategic macro-sequences (numbered by
    count within run-through, e.g. Draft1/RT2/Ma5 and Draft1/RT2/Ma6 in Fig-
    ure 25). Each macro-sequence involves the translator putting a medium-sized
    unit of text into working memory, then translating and/or revising it until it
    seems satisfactory and/or until the translator turns to another text unit. Figure
    25 shows two macro-sequences in which Fleur initially translates Lines 6 and
    7 respectively.

  5. A macro-sequence involves one or more strategic and/or non-strategic micro-
    sequences. With strategic micro-sequences, the translator identifies a discrete
    text problem, then seeks and evaluates solutions, and finally accepts a solution
    or abandons the search. In Figure 25, for instance, Fleur proposes the literal
    equivalent happiness for geluk, then realizes that the non-literal joy might be
    more appropriate (TU96), evaluates it (TU97), and finally leaves both alterna-
    tives on her written version (Figure 26). Non-strategic micro-sequences have
    no fixed pattern. Examples are Fleur’s TU108 Scan, where she starts a new
    macro-sequence by silently reading Line 7, thus putting it into working mem-
    ory; and her Spontaneous change of als rook into as smoke (TU99).

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