The Translators’ Personae: Marketing Translatorial Images as Pursuit of Capital1
Bibliographic record
Abstract
This paper examines the collective self-images of Israeli literary translators, assuming that their desired idealized personae are no less effective than their actual performances in regulating the “rules of the game” in their field. In view of translators’ popular image of ‘invisibility’ and ‘submissiveness,’ my argument is that translators are compelled to make intensive use of self-promotional discourse in their endeavor to establish their profession as a distinctive source of cultural capital. The present analysis is based on around 250 profile articles and interviews, reviews, surveys of translators and other reports in the printed media from the early 1980s through 2004. Three main self-images emerge from this self-presentational discourse: (1) The translator as a custodian of language culture; (2) The translator as an ambassador of foreign cultures and an innovator, and (3) The translator as an artist in his/her own right.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".