Bibliographic record
Abstract
The motto is a potent marketing tool in today’s globalized site of higher education. Beijing Foreign Studies University (BFSU) adopted a new motto in 2011 to reflect its new self-branding as a cosmopolitan scholar-doer. Its English translation has since then triggered much discussion about quality assessment. The current study critically surveys the existing literature on translation quality assessment (TQA), in an effort to identify an appropriate framework to assess the translation of Chinese university mottos. House’s model (2015) is found the most appropriate and applied to the official translation of the BFSU motto, after being adjusted in two important aspects. One, in regard to the rise of English as a language of global communication, it is proposed that more broad-based English norms than those of English as a native language be established for the purpose of adjudicating cultural filtering. Two, the use of corpus-based contrastive pragmatics is expanded to gauge the justifiability of overt as well as covert mismatches. While the errors identified by such a modified model are better intersubjectively verifiable, it remains to see how social research can be incorporated into the system to assess the degrees different errors may impact on the perceived quality of a translation.
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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".