Representing, Narrating, and Translating the Syrian Humanitarian Disaster in The Guardian and The New York Times
Bibliographic record
Abstract
Many scholars have attempted to understand certain aspects of translation and its fundamental role in constituting reality and representing the Other during media news coverage of international events. However, translation is often an invisible activity during such coverage. The relationship between translation and representation of the Other in the global media and news texts raises ethical questions about translation and textual manipulation. This dilemma is reinforced by the media’s selection of specific quotations and narratives for translating and publishing. It also imposes the question of media responsibility and translators’ ethics towards representing the Other, especially when the media deal with international events. The majority of media codes of ethics do not mention translation as a fundamental factor in ensuring and maintaining news accuracy and objectivity as well as fair representation of the Other. This paper scrutinizes media responsibility and translation ethics based on The Guardian and The New York Times’ representation of the Syrian humanitarian disaster (SHD) as embedded in the translated quotations and narratives told by Syrian citizen journalists (residents, refugees, protesters, eyewitnesses, and activists). To do so, it draws on Mona Baker’s narrative theory, on Stuart Hall and Edward Said’s theory of representation, and on media responsibility and translation ethics theoretical approaches. Accordingly, the corpus consists of 326 news texts distributed as follows: 177 news texts from The Guardian and 149 news texts from The New York Times. This represents a three-year timeframe of the SHD, from March 2011 to February 2014. The findings provide further understanding of the media’s responsibility in representing the events of the Other and translation ethical practices in the text.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".