The T/Daos shall meet: The failure and success of English transliterations of Mandarin Chinese
Bibliographic record
Abstract
When a Canadian exchange student returns home from a semester abroad in the capital city of China, she might tell her friends that she had Peking duck every day, but she would never, as a 21st-century liberal arts student, say that she stayed in Peking for a semester. Rather, she would say Beijing, as would most English speakers in the present day. But such discrepancies between English transliterations of Chinese words are far from uncommon. Is it the Nanking Massacre or the Nanjing Massacre? Who is the author ofTao Te Ching: Lao-Tzu or Laozi? What, then, is theDaodejing? This paper will focus on the English representation of Mandarin Chinese phonology, particularly the consonant sounds. The inconsistency of English transliteration of Mandarin is caused by historical exchanges and encounters between the British and the Chinese and a lack of a monolithic standardization of Mandarin. Paradoxically, while these transliterations attempt to unify and standardize themselves and the representation of Mandarin sounds, they simultaneously represent the concept of a diverse Mandarin.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".