Translation Trainees’ Self-censorship and its Pedagogical Implications: A Triangulated Investigation in Hong Kong
Bibliographic record
Abstract
Employing a triangulation method based on interviews, performance exercises, and think-aloud protocols (TAPs), this study probes the thought processes of ten Hong Kong translation trainees of diverse backgrounds to identify possible self-censorship during translation. The two English source texts for the performance exercise contained sensitive language or content with respect to sex and the trainees’ home country. A text containing mild criticism of Christianity was used as a control. It was found that most respondents were aware of the relationship between ideology and translation, and employed a semantically literal translation approach to render the ideological items in full. They took a broad range of factors into consideration, including translation purpose, text type, register, and target readership. Only one respondent self-censored his translation, while three others revealed their concerns about censorship during the TAP. The results show that the trainees share the same social habitus and highlight a need for ethics-awareness training in the translation curriculum. Given the current lack of such training as revealed by other surveys, recommendations for curriculum design are made. The influence of habitus on self-censorship could be further explored by comparing, for example, translation trainees and professional translators. The research methodology could also be replicated in other cultural and linguistic settings for a broader understanding of this topic.
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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".