Quotation as a Key to the Investigation of Ideological Manipulation in News Trans-Editing in the Taiwanese Press1
Bibliographic record
Abstract
News trans-editing, which has gate-keeping and adaptation as distinctive features, is widely adopted by news organizations to produce suitable target texts. Since news organizations are socially, politically and economically situated, news trans-editing is always mediated in one way or another. Using the trans-editing of quotation as a key, this paper conducts an empirical case study and investigates how the target newspapers’ ideologies systematically manipulate the seemingly “objective” trans-edited news texts. The case study data covers some news texts concerning China’s anti-secession law from theNew York Timesand theWashington Post, and their trans-edited Chinese versions from theChina Times, theUnited Daily Newsand theLiberty Timesin Taiwan. After introducing the relevant contextual factors, a comparative study of the source and target texts is made in terms of the following four aspects of quotation to identify recurrent shifts: quotation modes, news sources, quotation contents and reporting verbs. By analyzing ideological reasons behind the recurrent shifts against the contextual factors, this paper elaborates on the target newspapers’ ideological manipulation with practical examples.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 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".