Bibliographic record
Abstract
Traditionally, the ideal translation of an original text had been considered to be transposed into a target language as faithfully as possible. However, in recent years, translation has gradually come to be understood as a process of mediations between cultures and languages rather than a process of matching up one word in a source language with another in a target language. Further, as Lefereve claims, there may be little intrinsic value in any original text. What there is though, is context: conventions, traditions, trends, fashions, power plays that any social context will bring and that will determine what publishers sell and people read(Flotow 2005: 45). At a time like this, feminist translation was born in Quebec in the 1980s as a consequence of the experimental writing of female writers with feminist perspectives. Unlikely other theories in translation studies, this theory and practice are thought to go together. This study aims to introduce one of the feminist translation strategies, ‘hijacking,’ which allows feminist translators to make their works visible by the deliberate appropriation of original texts, to be on the lookout for this strategy in the translated texts of a literary work, and to consider whether the hijacked text could be a faithful translation or not. To do this, this paper studies the translated texts of two male translators in terms of ‘incest,’ ‘paedophilia,’ and ‘sexuality’ in Tony Morrison’s The Bluest Eye.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".