The Rhetorical Communication of Identification Theory in the Translation of Chinese News into English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To further understand the external news publicity effect of mainland media, the open-source data mining platform Social Bearing is applied to collect data of China’s 2021 Two Sessions on Twitter platform and relevant reports of the official accounts of Xinhua News Agency, China Global Television Network and People’s Daily, together with reports on 2020 Two Sessions. It is found that the topics chosen by mainland media are different from that of the Western media, and the acceptance of the reports of the three central mainland media in foreign readers is not remarkably high. Based on this research, this paper applied Kenneth Burke’s identification theory into news translation practice. It shows four translation approaches such as literal translation, amplification, omission, and adaptation can be used in the process of news translation to achieve narrative identification. Results of this study have some significance to translation for China’s global communication.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it