Journalistic translation: A gate at which journalism studies and translation studies meet
Bibliographic record
Abstract
This theoretical article focuses on a part-whole relationship between journalistic translation (or news translation) and the overall process of news gatekeeping within and outside of the boundaries of media organizations. The article argues for understanding the part (i.e., journalistic translation) in light of the whole (i.e., news gatekeeping) and the whole in light of the part. In this regard, it approaches journalistic translation in terms of its relationships with other news gatekeeping operations (e.g., information selection and editing) and factors that control the whole process of news gatekeeping (e.g., organizational and social constraints); besides, it touches on the relevance of the inclusion of the concept of translation in theorizing about news gatekeeping and the potential of the findings of research on translation in news production settings to contribute to news gatekeeping scholarship.
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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 | 0.073 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.014 | 0.094 |
| Scholarly communication | 0.035 | 0.044 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".