Translators Talk about Themselves, Their Work and Their Profession: The Habitus of Translators of Russian Literature into Hebrew1
Bibliographic record
Abstract
In his discussion of the habitus of translators throughout history, Simeoni highlights the submissiveness and invisibility associated with their inferior position and with their tendency to assimilate and internalize these views of their professional activities. In keeping with recent reappraisals of this position, the present paper examines the ways in which translators of Russian literature into Hebrew, from the 1970s to now, present themselves, their work and their profession—and reflect on their habitus, their conduct in the system of Russian literature translation, and their practice. From the theories of Bourdieu and of Even-Zohar, we explore these self-representations, and find that rather than presenting themselves as invisible, passive and professionally indistinct, these translators make a point of announcing their presence as well as of emphasizing their work. While they adopt different models, they nevertheless share a repertoire and both a social and a professional habitus—one that is a prerequisite for entering the field of literary translation, and particularly the subfield of literary translation of Russian literature, and for operating successfully in these arenas. It is in this way that they achieve status in the culture, accumulate capital and construct their (distinctive) group identity. In addition, the discourse of Russian literary translators points to the dynamic nature of their system and helps push it towards the center of the polysystem of Hebrew translated literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".