Bibliographic record
Abstract
Translation scholars have discussed the changing role of translation in the transmission of international news information and called for news translators to adapt to such changes. However, the previous discussion has neglected to address the emerging phenomenon of market-driven journalism and its implications for news translation. Ratings-conscious news stations are beginning to revise news agendas and editorial priorities with a view of competing for audiences. Translators who work in newsrooms also assume a role that is traditionally associated with journalists. The rise of market-driven journalism affords scholars the opportunity to consider how the changing ethos of journalism alters news translation strategies. Furthermore, this change forces a rethinking of some earlier assumptions regarding the nature of translation. Although the prevailing trend of market-driven journalism crosses over different media types, this paper primarily centers on the case of television journalism. By examining authentic broadcast news items that were collected from a commercial television news station in Taiwan and interviews with senior TV news translators, this paper unveils a new profile of television news translators in a news ecology that is defined by market values.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".