Bibliographic record
Abstract
The paper aims at showing that the journalist-translators’ decision-making with respect to what is to be included or left out of a target text, in the limited space provided by target newspapers, is governed by background knowledge considerations which reveal awareness of current political routines – in addition to generic constraints, narrative priorities, language-specific preference, etc. This is a pragmatic level of meaning which contributes to realizing the intention of the text producer. The paper examines two source text/target text pairs of articles on Tony Blair’s premiership, from The Guardian and The New York Times (2007), translated into Greek for Η Καθημερινή ( I Kathimerini ) broadsheet newspaper. It presents an overview of linguistic/cultural shifts which ensure acceptability in the target text, and shows that information selection/reduction adheres – inter alia – to political theoretical background knowledge: in this case, it assumes perception of the notion of political representation, which may vary across cultures, and awareness of the features of presidentialism according to Heywood, which parliamentary executives’ conduct often exhibits. Findings underline the multi-faceted task of journalist-translators and call for a multidisciplinary approach to news translation, which would encompass political theory perspectives, in addition to linguistic and journalistic perspectives to variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".