Schäffner, Christina and Bassnett, Susan (eds). 2010. Political Discourse, Media and Translation. Newcastle upon Tyne: Cambridge Scholars Publishing. (Book Review)
Bibliographic record
Abstract
Are we well informed?This is the (provocative) question that one might raise after reading this volume, a collection of articles tackling the notion of recontextualization in news translation, with a special focus on political discourse.The book, which provides some answers to the intricate relationship between translation and media, investigates the complex (and most often invisible) work of news translators.Journalists, as well as media in general, play the crucial role of mediators between institutions, their representatives and the public.Our knowledge of governmental activities, political communications, public addresses, etc., entirely depends on media.When translating some news in order to report from foreign countries, journalists filter, select, add, omit and adapt information, which contributes to releasing some localized news version for the new target audience.In brief, they recontextualize it.But, one might wonder, are they really conscious of their power, their agency, the consequences, effects and impact that such manipulations of the information can have on the public?Although, on the one hand, journalists tailor the way we come to grips with a culture, hardly ever are they trained as translators, and as a consequence, they perform their task without bearing in mind some of the traditional concerns of Translation Studies (TS).One such concern is the issue of fidelity between source and target texts.Who, then, dictates the rules of the(ir) game?One could argue that newspapers and TV political orientations, as well as advertising investors' economic interests-to name a few-do indeed influence news translation.However, they are just the tip of the iceberg of a (much wider) manipulative process.The reasons for such transformations, and the way recontextualization and/or localization are carried out, perhaps, are more intricate than it might initially seem, and may be grounded in arbitrary decisions: namely, decisions made by those who select the information to be translated from the very start on the basis of specific, yet subjective, strategies.But, what are these strategies or techniques?Who revises the 'final product'?How does reception influence decision-making?Why are articles seldom labeled as translations?Why do journalists rarely define themselves as translators?Political Discourse, Media and Translation is the printed outcome of a symposium held in February 2007 at Aston University.The collected papers shed light on the above questions in a somewhat provocative fashion intellectually speaking.Their overall goal is not to cast doubt on or assess the profession of journalists as translators, but to demonstrate that the principle of equivalence applied to news translation does not serve TranscUlturAl, vol.5.1-2 (2013), 219-226 http://ejournals.library.ualberta.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.035 |
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".